STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion
Authors: Xurui Li
Yue Qin
Rui Zhu
tianqianjin lin
Yongming Fan
Yangyang Kang
Kaisong Song
Fubang Zhao
Changlong Sun
Haixu Tang
Xiaozhong Liu
Venue: EMNLP 2023 Main (2023)
Keywords: semi-supervised
text-graph joint learning
risk detection

Abstract: Commercial news provide rich semantics and timely information for automated financial risk detection. However, unaffordable large-scale annotation as well as training data sparseness barrier the full exploitation of commercial news in risk detection. To address this problem, we propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph (NEKG) to endorse the risk detection enhancement. The proposed model incorporates a label correlation matrix and interactive consistency regularization techniques into the iterative joint learning framework of text and graph modules.  The carefully designed framework takes full advantage of the labeled and unlabeled data as well as their interrelations, enabling deep label diffusion coordination between article-level semantics and label correlations following the topological structure. Extensive experiments demonstrate the superior effectiveness and generalization ability of STINMatch.

STINMatch:
Semi-Supervised Semantic-Topologiaal Iteration Network for Financial Risk Detection via News Label Diffusion
Xurui :i'; Yue Qin Rui Zhu', Tianqianjing Lin', Yongming Fan', Yangyang Kang', Kaisong Song" Fubang Zhao', Changlong Sun', Haixu Tang', Xiaozhong Liu
Alibaba Group, China, Indiana University Bloomington, USA, Zhejiang University, China,
Purdue University, USA, Northeastern University, China, Worcester Polytechnic Institute, USA
xurui. msm.com, qinyue (rryuu@@iu.eduu ed. zhul I @ iu.edu, mmnj@zzj.eduu.oo fam322 @ purdue.edu, xliu @4@ wpi.edu [yangyang.kange kaisong, sks, fubang...gg.D z/b Pealibaba-ine.com changlong sel taobao.com, hatang @ indiana.edu
Abstract
sive and timely information embedded in news ar- ticles (Walker, 2016; Calomiris and Mamaysky, 2019; Li et al., 2022). Nevertheless, challenges are persisting on how to efficiently utilize news for detecting financial risks for enterprises.
Commercial news provide rich semantics and timely information for automated financial risk detection. However, unaffordable large-scale annotation as well as training data sparseness barrier the full exploitation of commercial news in risk detection. To address this prob- lem, we propose a semi-supervised Semantic- Topological Iteration Network, STINMatch, along with a News-Enterprise Knowledge Graph (NEKG) endorse the risk detection enhancement. The proposed model incorpo- rates label-correlation matrix and interactive consistency regularization techniques into the iterative joint learning framework of text and graph modules. The carefully designed frame- work takes full advantage of the labeled and unlabeled data as well as their interrelations, enabling deep label diffusion coordination be- tween article-level semantics and label correla- tions following the topological structure. Exten- sive experiments demonstrate the superior ef- fectiveness and generalization ability of STIN- Match'.
One of the key issues is the multi-label diffusion problem. One news document may carry multiple risk labels (e.g.. 'debt risk' can be accompanied by 'litigation threat"), and conventional methods difficult to handle the mutual influences among these labels given the rapid growth and variety of streaming media. Recently, deep learning methods have achieved great success in the field of natural language processing. Deep multi-label text classifi- cation (MLTC) methods can be applied to explore the label correlations (Liu et al., 2017; Yang et al., 2018) 8). Unfortunately, pure text-based methods can- not handle risk diffusion in the business ecosystem. In order address this problem, we propose a new model, semantic-topological iteration network (STIN), to estimate the 'risk diffusion'' on the estab- lished news-enterprise knowledge graph (NEKG). Unlike previous graph neural network (GNN) meth- ods for text analysis (Yang et al., b,,; Pang et al., 2022; Zhao et al., 2023), our STIN model fo- on the multi-labee-correaatiee guided text-graph joint learning, hoping to capture the dissemination for various types of financial risks following the NEKG topological structure.
1 Introduction
Financial risk detection for enterprises is an essen- tial task to assess and estimate the dynamic fragility of the market. Efforts need to be made to probe the vulnerable enterprises and enable timely pre- paredness. Traditional methods often treat each enterprise individually and leverage the official information or relevant structured data from gov- emmment agencies to assess risk (Ozbayoglu et al., 2020). However, these official data are often bi- ased and lagged, making it difficult to identify risks accurately and timely (Bi et al., 2022). Commer- cial news mining offers another effective perspec- tive for financial risk detection owning the mas-
Another challenge is the limited annotation data of financial news due to domain expert scarcity or expensive labor cost. Semi-supervised learning (SSL) is a common method for solving data scarcity problems. Many SSL methods based on entropy minimization, consistency regularization or generic regularization have been proposed for low-resouree analysis scenarios (Berthelot et al., 2019, 2020; Sohn et al., 2020). However, few studies have been carried out on the semi-supervised integra- tion for text-graph joint learning framework, which could improve the performance for both modules
These authors contributed equally this work.
Corresponding authors.
htwp:/hhww.ooomuur//////eeem Financial-Riss -isiiill-------eee Siisk-Detcctionggit

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by leveraging the unlabeled data more efficiently in scenarios similar to our task.
2017; Alkhereyf and Rambow, 2020) enhance node representation, but the text representations are fixed during training. Some recent works are combining GNNs with text classifiers to take ad- vantage of both topology and semantic modeling. For example, GLEM (Zhao et al., 2023) proposes variational expectation maximization framework to alternatively updates the text and graph modules separately. Nevertheless, different from previous works, our STINMatch method focuses on the semi- supervised integration for text- text-graph joint learn- ing framework, as well as the multi-label diffusion upon typologies for text-attributed GNN works.
The contributions are summarized as follows:
We propose a pioneer semi-supervised text-graph joint learning framework STINMatch. It fully ex- ploits the semantic information and topological as- sociation for risk diffusion with limited annotation data.
A novel content-label-ooollog mechanism is further introduced during the iter- ation of STIN model to handle the multi-label dif- fusion issues on text-attributed graphs.
aggregation
We release an NEKG dataset annotated with mul- tiple financial risks, which leverages real-world enterprise relatedness and news-enterprise associa- tions for risk detection.
Consisteney Regularization for SSL. Consistency regularization is a popular SSL approach to con- strain model predictions being invariant to input noise. MixMatch (Berthelot et al., 2019) applies data augmentation techniques and introduces uni- fied loss for unlabeled data that scamlessly reduces entropy while maintaining prediction consistency. The modified versions such as ReMixMatch (Berth- clot et al., 2020) and UDA (Xie et al., 2020) both use beakly-augmented examples to generate artifi- cial labels and enforce consistency against strongly- augmented examples. FixMatch (Sohn et al., 2020) is simplified version of ReMixMatch and UDA, which combines the pseudo-labeling with consis- tency regularization while removing many speci- fied components (e.g.,training training signal annealing and distribution aligmment). However, all these meth- ods focus on SSL within a single text or graph mod- ule respectively and could not be trivially adapted to our joint learning framework for text-attributed graphs. Other related work related to classifica- tion for subjective texts in different granularities include (Xiao et al., 2019; Moon al., 2021; Song et al., 2023).
Extensive experiments demonstrate the detection effectiveness of STINMatch and its good general- ization ability on NEKG dataset, as well as other two public datasets.
2 Related Work
Financial Risk Detection. Classification and re- gression algorithms as well as time series forecast- ing have been widely used in financial risk detec- tion (Ozbayoglu et al., 2020; Sezer et al., 2020). However, such methods mainly rely on historical, structured data from corporate government agen- cies which lack up-to-date information. Recently, unstructured textual data, such as business man- agement reports and financial news, are adopted for financial risk detection due to richer informa- tion and better timeliness (Peng and Yan, 2021; Li et al., 2022). However, such methods overlook the interactions between news and enterprises for risk diffusion by simply leveraging sentiment analysis each isolated document. A recent work (Bi et al., 2022) leverages financial news intermediaries between enterprises to exploit their interactions, but textual contents of the news are neglected.
3 Preliminary
In this section, we introduce the task goal of STIN- Match, detail the NEKG construction, and provide an intuitive example for financial risk diffusion.
Label Diffusion. Label correlations (Kurata et al., 2016; Yang et al., 2018; Zhang et al., 2021) are widely employed to improve model performance for MLTC tasks. GNN methods have also been used to deal with label diffusion issues for text analysis. Most of them apply GNN on the ex- tracted word/entity-level knowledge graph (Yang al., 2021b) or label co-occurrence graph (Pal et al., 2020) to enrich representation for each in- dependent sample. Other GNN methods utilize text-attributed node relatedness (Kipf and Welling,
Semi-supervised Risk Diffusion. Given a set of
news X and risk labels Y E {0, 1}K, the dataset D Dr U DU contains n labeled news Dr { y:)l E X,y, Y, 1,2,.... ... n) and unlabeled news Du {agli 1,2, m). The risk diffusion model aims to learn the mapping F X - from news to multiple financial risks. NEKG Construction. The news-enterprise knowl- edge base is formulated as a graph G = (N, C, E),

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where N is vertex set denoting news, C vertex set denoting enterprises, and E {ele (p.q),p E C,q () Ufele = (v, q) E N, q E C} is an undirected edge set. Among N, each news in D is annotated with K binary risk labels, represented by a K-hot vector.
tional MLTC methods only consider the content of the news itself. Suppose the no node are connected with three different enterprise nodes 02,3,4 accord- ing to co-oceurrence relations. Each enterprise node can be connected with other enterprise nodes according to the real-world enterprise relations (c.g. [<2, et), as well as some extra news nodes (e.g. Lea, R311). The risk information from expanded news can be propagated to the center news node no through the jump enterprise nodes. If most of the neighbor news contain the risk information of Liquidation, the probability for the target ni news having Liquidation risk label also increases. On the other hand, the probability distribution of risk labels for each enterprise node can also be obtained when the diffusion model converges.
Resource and Statistics. Specifically,, our NEKG contains 99,666 news nodes and 50,193 193 enterprise nodes. Each news node is initialized by its title and content, and each enterprise node is initialized by the company name. The edge between a news node and an enterprise node indicates that the enterprise is mentioned in the content of the news. The edges between different enterprises belong to five real- world relationship types: subsidiary, investment, share-manager, share-investor, and share-legal- entity. In total, NEKG contains 135,340 news- enterprise edges and 121,938 enterprise-enterpiin edges.
STINMatch: Methodology
4.1 Overview
Annotation. We sample [r. = 15, 000 news from all news data, and D2 is annotated by three do- main experts. Each news can be identified as cor- relating to one or more financial risks from the following labels: Bankruptcy, Liquidation, Busi- ness closure, Production halts, Debt, Corruption, Dispute, Counterfeit, Fraud, and Litigation. The annotation standard is summarized through three preliminary rounds of annotation with 500 pieces of news. After the adjustment through preliminary rounds, each annotator labels Dr independently in more than month's 's time, and the annotation results achieve a 0.803 Fleiss's s kappa.
Architecture. We propose an end-to-end semi- supervised semanticttopologict iteration network to endorse multiple risk diffusion. As illustrated in Fig. 2, STINMatch is composed of text clas- sifier (i.e., MM), a GNN-based node classifier (i.e.. Mg). and a label-correlation matrix IR. M2 con- tains a base text encoder and several classification layers. It takes textual inputs and learns text embed- dings through classification objectives on labeled news. The hidden representations and predictions of M, are utilized to initialize node features of My at each iteration round. M, propagates risk infor- mation from labeled samples to unlabeled samples through NEKG topology to achieve risk diffusion and boost risk detection. We iteratively train M, and My in turn until convergence, and we seek to optimize the integration of the correlation ma- trix R into an iterative joint learning framework in order to maximize performance and efficiency. Upon the diffusion model reaching convergence, the enterprise risk labels are adopted as signals for precisely quantifying risk evaluation. Our work advocates for an innovative approach to financial risk detection that integrates label correlation ef- fectively into the text-graph iterative learning pro- cess. The integration of label correlation into the iterative lcarning process offers mutual benefit, significantly improving the overall performance of the system. On the one hand, the label correlation, serving as domain-specific knowledge, plays an im- portant role to guide the label diffusion for both text and graph modules in each iteration; on the other
Semantic labels from zews ibseé
NEWS
Topological labels
C
from diffusion
Real WOC cooguty mnladioo
Figure toy example for financial risk diffusion through NEKG. NEKG consists of two node types: text- attributed news nodes and intermediate enterprise nodes.
Intuitive Example. Fig. 1 shows a toy example for explaining the financial risk diffusion through NEKG. The goal of model is to predict the risk label distribution of the target news node no- Tradi-

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STINMatch Framework
Semantic- Topoooocooollllccalll Iteration Network (STIN)
Buratien Sprei
Text Encoder ecari.)
fabeled
labeled
hg
Expend by Erngh
untabeled.
unlabeled
FC
PC2
OokY
Triiaiiw
Elexated
4
Train
Constralntt
FC
Interactive consistency regularization text and graph module
(a)
Teat Model
Graph Model M
(a) shows the overall semi-supervised learning framework of STINMatch. (b) shows the deep interactions between text module, graph module and label correlations during the iteration for STIN model.
Figure 2:
hand, the predictions of the successively enhanced model help involve more previously unlabeled sam- ples into the calculation of label correlation, result- ing in more generalized label correlation in each iteration round.
With the retrained M, and Mg. STINMatch filters An confidently-predicter samples from m unlabeled samples to update the risk label correlation matrix E Rkxk as Ryj where N n + An the number samples for the joint set of DL and the filtered set, and
cos((yn: 925, WNz), (y1j, M2j............. (y1s, U2i. UNi) is the vector consisting of the i-th risk labels for the N samples. IR is initially calculated from the labeled set Dr and updated in each iteration round. Fig. 3 shows visualized calculation process for R.
Iterative Learning Framework. Below we elabo- rate on the itcrative supersupe learning frame- work and the workflow from one iteration to the next iteration. We ignore the subscription denoting the iteration round for readability.
For certain iteration round, denote the key information layer in M, h4. hy carries em- beddings: text semantic embedding It, and graph context embedding Tg from the last iteration. Mr performs data augmentation techniques on both labeled and unlabeled data to compute supervised and unsupervised loss functions for updating model parameters. We detail the module components and semi-supervised learning strategy for M, in section 4.2.
4.2 Semi-Supprvised Text Model
Text semantic embedding it for Mt comes from a text encoder (e.g. CNN, RNN or BERT-based). Graph context embedding T. is randomly initial- ized for all nodes and can be obtained from ei of graph model M, in the following iterations.
Similar to (Kurata et al., 2016), involve weight initialization strategy leveraging label co-occurrence to improve the model performance for MLTC task. Let 1,- denote the second-last layer in M,. The text model makes predictions as le = (W, @ R),- + b, where I is the risk label correlation matrix, @ represents element-wise pro- duction, and W, and b, are learnable parameters.
After retraining Mr. we take the value of its representation layer 3 as the learned text repre- sentation initialize the node representations hy Mg in this iteration round. The Mj's prediction 4 = (p will also participate in the calculation of graph aggregation process. Similarly, we train Mg with a joint loss function, consisting of supervised loss and an unsupervised loss, and take the values of its last hidden layer bi learned node representations. Then use bi reinitial- ize the v, of M, for the next iteration round. We detail the semi-supervised learning of Mg and data filtering strategy in section 4.3.
Let X ((26, y6) b (1,..., B)} denote batch of labeled samples, where 24 are training examples and y/ are multi-hot labels. For the k-th independent risk label, the supervised loss on the batch of labeled data is:

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Senumtic-repreesnttttion and Label-distribution guided Attention (SLA) layer. News semantics, la- bel correlations, and risk diffusion can be learned jointly via NEKG by stacking SLA layers. The following part introduces the forward calculation from the imput node feature set h2 of the /-th SLA layer to that of the next layer h/41 Below we omit the subscription denoting the graph model g and the number layer for readability. For each node μ,, the input and output node features for SLA layer are denoted as hy and lig.. respectively.
Ci F bi log P.A + (1 v1,)) log(1 - p.a)]
Here Pi,s is the probability of the i-th sample being predicted with the k-th risk label by Mr.
Let {us E (L....,,')) denote batch of unlabeled samples. STINMatch applies differ- ent augmentation techniques on the unsupervised data L/ to generate set of weak augmentation data 4 and a set of strong augmentation data Ui, by manipulating the primary semantic representation and the supplementary graph context represen- tation Zg for Mr- Let lit (p"'. Pp ...... ..., )² and if P2. : px)² )r² denote the predictions of Mt 24" and and respectively. To generate 47", the key idea is only disturb the supplementary graph context embedding "g for weak augmentation. We apply the Random Perturbation method (Kumar et al., 2019) on 2g and combine it with the original as the input of FC1. For 1ł, we applied relative strong augmentation method Extrapolation (Kumar al., 2019) both 24 and Zg. which utilizes the differences from other samples to synthesize new examples. Here, the weak augmentation provides higher accuracy for the pseudo labels, while strong augmentation provides better diversity and a larger region of sample perturbation for the consistency regularization, thereby improving the performance of the semi-supervised learning.
Specifically, we first apply a multi-head attention mechanism similar to (Veličković et al., 2017). It learns J independent semantic attention weights to stabilize the learning process, where the j-th single-head weight between node f and is calculated as:
semantic-similarity--nn
p
m/h. LeakyReL U(a (W=S) [Wihi]|W>hh.)]
where W is shared transformation matrix, a² a shared reed-forward neural network for each layer. and lI the concatenation operation.
Moy-4bnn Rry
Bontenggey
Lipiiiation
Complion
Label Set Size K
Sauple Size
Labelled Sauple
For each pk: calculate pseudo-label with an indicator function / 11 10% > r] which returns I when 2\x > r else 0, where F is threshold hyper- parameter. Let p = { | = ... K}. The unsupervised loss for k-th risk label on the batch of unlabeled data is defined as:
16x)
Stmm wtw.bereeeuuttyygggg.e..... Lug.udatuuuii--- Sun.Uurreenntttn Compatoo)
Winy Win,
Ta diffinien
B'
K
1
Σ
P
1] LEK
2us round of ditiesion
B'
where 1[EK P.t is an indicator function for verifying the validity of the predictions the i- th augmented sample and LÄR,, is the cross-entropy loss on k-th label for the i-th unlabeled sample:
Figure 3: (a) shows the calculation process and a visual- ization of label correlation matrix IR. (b) is an example showing how does R affect the risk label diffusion.
Then we involve a label-similarity atten- tion mechanism since there exist internal relations among different labels. However, direct similar- ity calculation between multi-hot label representa- tions neglects such correlations. To address this issue, we utilize the correlation matrix I to cap- ture the internal relations among different labels to enable cross-label similarity calculation. As shown in Fig. 3, without considering label cor- relations, the aggregation weight W(ng + et)
Li = 1.,, log PI + (1 P. log pf,+)) Finally, we merge the supervised and unsu- pervised loss of all K labels for training Mú; C. -EK [G,, + 7 where y is hyper- parameter.
4.3 Semi-Supervised Graph Model
Content-Label-Topolee Aggregation. The ag- gregation mechanism in M, is dominated by

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is equal to (nn + a). While considering that Bankruptey and Liquidation are correlated risk labels, W(n + e1) becomes larger than W(n1 + c1). The label attention between node and node v is:
dropout, we further apply the Extrapolation aug- mentation method (Kumar et al., 2019) on the input node feature set hg of the graph model.
For each lk we also calculate a pseudo-labes as 0% = 1 > T], and let Q" = { K}. 1.2....,K] dictions from Mg. we introduce an Elevated Con- straint by restricting that the additional informa- tion from neighbors do not reduce the risk labels obtained from text model M, for each node itself. Namely only the samples whose pseudo-labels pre- dicted from M, being a subset of that from graph module Mg will participate in loss calculation, and the unsupervised loss for the k-th risk label to train Mg on the batch of unlabeled data is defined as:
=u==11e
C/ ④ GR| Frobenius
To ensure the validity of the pre-
Note that l1 and ffr come from the predictions of Ms @ represents element-wise production, and ||-I Frobenius represents frobenius norm for matrix. After obtaining both M,, and Spp. we combine them into merged attention weight a. The j-th merged attention part of node μ with respect to node is the softmax of linear combination:
Lne exp[> The + M)- By,cl
ErEN, exp[>' Tpp-- + (1 2) sp.r]
where N₁. indicates the neighborhoods include it- self for node μa and X E R. is trainable vector. At last, the SLA layer outputs the feature represen- tation for node μ as the concatenation of J inde- pendent transformations:
5,6 = Σ
Σ
C. > 1) (P & Q²)) D
Here A represents the simultaneous satisfaction for both conditions. ", is the cross-entropy loss for the i-th unlabeled sample, regarding d% as the label and q as the prediction:
-
i j=l
E VEN,
anu Wh Em
k. 10% log of + (1 (1, log k.))
Finally, we merge the supervised and unsuper- vised loss of all K labels for training My: Lg
Here, a is the sigmoid function. Note that the input feature for the first SLA layer is initialized from the learned text representation th in this iteration round, and we employ the averaging operation in- stead of concatenation for the J head outputs on the final (prediction) layer as in (Veličković et al., 2017).
K [ +
5 Experiments
5.1 Experimental Setting
Datasets. We validate model on three different datasets. The main dataset NEKG are described in section 3. Note that we fixed a 5000 original labeled news as test set. We take only a random part of the remaining original labeled news as our labeled data set Dz for each experimental setting, We remove the labels except for D,, during training STINMatch. The default labeled size for Dz, is set to 1000, unless otherwise stated. The enterprise- related information comes from a subset of the data integrated by our data center team collected from various of government-backed open data providers such as National Enterprise Credit Infomation Pub- lic System of China, and it enables the designed web-crawler to collect the corresponding news for annotations and evaluations.
Consistency Regularization for My. Similar to Mr, the supervised loss for the k-th risk label to train Mg on the batch of labeled data is:
C = Σ & logo, 9 + (1 - B1xx) g.,)),
where A.A the probability for the i-th sample being predicted by M, as the k-th risk label.
Also, STINMatch applies data augmentations on the unsupervised data U to generate set of weak augmentation data un and set of strong augmentation data Un for Mg. Let po "f.'......... .")" and 17 - -qq- ...... ..kkkk)) note the predictions of M, on 4. and 2g. re- spectively. For 15 utilize the graph attention dropout on the attention coefficients in (Veličković al., 2017) at inference phases. The weak aug- mentation only changes the aggregation pattern without disturbing the input node features. For strong augmented 1s, besides the graph attention
) de-
The other two public datasets RentThe Way and Goodreads-Spoite will be described in the algorithm generalization part of section 5.4. Implementation We carried out all models with Pytorch. Graph model is implemented using dis-

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tributed graphics library (DGL). All models are trained on the NVIDIA Tesla A100 80GB GPU. The hyper-parameter details are shown in Ap- pendix A.
Table 1: Comparisons on news risk detection task be- tween STINMatch model and other methods. indi- cates that pre-trained BERT embedding layer serves as the first layer of the model.
Category
Model
MacroF1
MicroFI
5.2 Baselines
FasTTex
0.62
0.81
As shown in Table 1, the STINMatch model is compared with baselines from 4 main categories. Category contains classical text classification methods. RoBERTa-sw represents the RoBERTa trained using the sliding-window method (Wang et al., 2019). In order to validate the performance of different frameworks over pure text level fairly, most of the text encoder part in following cate- gories are based on the TexCCNN over pre-trained BERT embedding layers (represented by TCB). Category 2 contains state-of- ofte--of-the-art the-art text models specialized for MLTC tasks such as LCNNI (Ku- rata et al., 2016), SGM (Yang al., 2018) and CORE (Zhang et al.. 2021). Focal or DB loss (Huang et al., 2021) designed to handle the label distribution for MLTC tasks from loss terms are also tested.
TexRCNN
0.728
0,906
Tex(RCNNt
0.748
0,885
Category
TexiNNN-atenntion
0,746
0.863
TexICNN (i.e. TCR)
0.808
0.922
RoBERTa-sw
0.815
0.902
LCNNT
0.724
0.831
SGM
0.714
0.841
Category 2
CORE
0.834
0.920
TCB-Focal-loss
0.830
0.923
TCB-DB-loss
0.813
0.917
Pseudo- TCB
0.814
0.921
Category
MixMach-TTCB
0.818
0,919
ReMixMatch-TCB
0.821
0,921
FixMatch-TCH
0.821
0.923
GCN-TCB
0.811
0,908
Category 4
GrapSSAGE/TCB
0.827
0.916
GAT-TCB
0.831
0.921
MAGNET-TCB
0.835
0.923
LC-GAT-TCB
0.837
0,926
GLEM-TCB
0.849
0.927
Category 3 contains typical semi-supervised learn- ing methods for pure text models.
STINMatch
STINMatch
0.889
0.938
Category applies typical GNN-based methods over TCB representations. General GNN models such as GCN (Kipf and Welling, 2017), Graph- SAGE (Hamilton et al., 2017), GAT (Veličković et al., 2017) are used. GNN models specialized for MLTC tasks such MAGNET (Pal al., 2020) and LC-GAT (Xu et al., 2020) are also included for comparison. GLEM (Zhao et al., 2023) is latest text-graph co-training method, which has also been tested. Some details of GLEM have been modified to adapt to our scenario for fair comparison (e.g. using TCB as base language module, replacing the binary cross-entropy loss with multi-label loss).
Table 2: Evaluation for enterprise risk detection based on risk propagation results from different GNN models.
Model
Accuracy
Fl-low-risk
Fl-high-risk
GCN-TCB
0.668
0.786
0.264
GraphSAGE-TCB
0.677
0.792
0.270
GAT-TCB
0.695
0.807
0.281
MAGNET-TCB
0.684
0.798
0,274
LC-GAT-TCB
0.704
0,813
0,287
GLEM-TCB
0.716
0.824
0.295
STINMatch
0.732
0.834
0,310
compared to all baselines. It is owing to the multi- label-correlation guided text-graph joint learning, as well as the interactive semi-supervised learn- ing across text-graph models. Note that we run the experiments for 5 times and reported the av- erage performances. The t-test results show that the proposed model significantly outperforms the best baseline by 4.7%. The detailed ablation study results for STINMatch are described in Section 5.4. Enterprise Risk Evaluation. We also conducted comparative experiments for evaluating enterprise risk detection based on the label-diffusion results from NEKG. In fact, each enterprise node has a credit risk rating label provided by an authorita- tive rating agency. The credit risk rating labels
5.3 Effectiveness for STINMatch
News Risk Evaluation. Experiment results are reported in Table with the labeled size n set to 1000. From Category 2 of Table one can wit- ness slight improvements with some multi-label methods, indicating the usefulness of correlations among labels. Text-based semi remi-supervised meth- ods also slightly enhance the performances. GNN methods achieve certain improvements compared to that of pure text-based methods, showing the ef- fectiveness of the additional NEKG. The proposed STINMatch model shows convincing superiority

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can be divided into 2 different levels according to the rating scores: high-risk (1) and low-risk (in- cluding those with no risk) (0). On the other hand, the GNN-based models will also offer risk predic- tions for each enterprise node when converged. To make the two label systems comparable, we sup- pose enterprise nodes with more than one predicted risk labels from graph models are high-risk (I), while others are low-risk (0). We take the credit risk rating labels as ground truth, and calculate the classifier metrics (Accuracy and Fl for low- risk/high-risk class) according to the predictions from different GNN-based models.
Table 3:
Ablation results for STINMatch method. w/o means without using corresponding component.
Model
MacroF1
MicroFI
w/o iteration w/o updating matrix IR w/o label attention 3 w/o unsupervised loss for Mr
0.841
0.923
0.876
0.931
0.869
0.925
0.873
0,929
w/o unsupervised loss for Mg w/o elevated constraint
0.871
0,929
0.878
0.931
STINMatch
0.889
0.938
enhanced text classification methods without text- graph joint learning.
By comparing the results of Table 1 and 2, we can find that the model performance of the enter- prise risk classifier nearly positively correlated with that on MLTC task for news risk detection. The performance of STINMatch method exceeds all that from other LNN-based baselines on enter- prise risk evaluation task.
Time Cost & Labeled Size Analysis. Fig. 4 presents the model performance over the increase with training epochs and the size of labeled data. The left insets (i.e., (a) and (b)) show the time cost and Maero Fl when varying training epochs with the labeled size fixed as 1,000. With nearly three times of computation time cost, our method out- performs the TCB baseline by 9.5% on Macro FI. The right insets show the increase ratio of Macro Fl along with the labeled data size. The result of inset (c) is calculated from dividing the best Macro Fl of STINMatch by that of TCB, which are shown in inset (d). It can be found that the increase ratio is larger when the labeled size is smaller, while it decreases with the growth of the labeled size. It indicates the better superiority of STINMatch lower resource scenarios. It can also be observed from inset (d) that when the labeled size [Dz] is 1,000, STINMatch achieves performance which is approximately similar to that with |D] 10, 000. However, for the baseline TCB model, the perfor- mance of the model with |D,| 1, 000 is signifi- cantly lower than that with |D,] 10, 000. This indicates that the proposed mechanisms effectively reduce the dependency on labeled data, resulting in substantial cost savings in terms of annotation.
5.4 Analysis for STINMatch
Ablation Study. To investigate the contribution for each component in STINMatch, we conducted 6 different ablation studies. The first study was trained by patient epochs for text and graph mod- ule respectively with early stopping in single round without iteration. The second study only initialized the label correlation matrix IR once with- out the updating mechanism. The third study only considered the traditional semantic attention and ignored the label attention 3 for Mg. The follow- ing studies explored different components for the semi-supervised learning-
From Table 3 we can find that every intention- ally removed component leads to decrease in the performance of the STINMatch model. It indi- cates that the well-trained semantic representation and traph-based diffusion model can perceive im- portant reciprocal advantages from each other by leveraging the multi-label correlations. The two descriptions "w/o unsupervised loss" and "w/o un- supervised loss" indicate the results after removing the semi- semi-supervieeee supervised techniques from the text model and the graph model. It indicate that the interac- tive supervise learning across text and graph modules indeed make a better utilization for unla- beled data at different training stages. Moreover, the superiority for the text-graph joint learning can be indicated by compare the results of category from Table 1, which represent the semi-supervised-
A Aggorttmm Generalization We also processed two public datasets RentTheRun Way (Misra et al.. 201 8) and Goodreads-Spoilers (Wan et al., 2019) to demonstrate the generalization ability of our STIN- Match method when adapted to other domains. We claborate on the two review datasets in Appendix C.
For these two datasets, we fixed half of the sam- ples as test set, and randomly selected 10% of the remaining samples as labeled ones for semi- supervised learning comparison. We selected four typical baselines described in section 5.2. From Ta-

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Cost Tok Omik))
500
MLTC datasets and validate its good generalization ability.
1C31
118
Pag (19 ogeng
200
7 Limitations
200
Marroi
1M4
69
STINMatch leverages ceni-supervised learning techniques on semantic-topological iteration net- work. Our research demonstrates that both the joint iterative learning and interactive consistency regularization of the text and graph models bene- fit the risk diffusion. However, we acknowledge that the proposed mechanisms inerease the com- putation cost. For example, with the same train- ing epochs and batch size, the computation cost of STINMatch is nearly three times the simplest baseline (i.e., TCB). However, this higher cost is justified by the significant benefits it brings. In fact, STINMatch outperforms TCB by 9.5% in terms of Macro F1, which is a substantial improvement. Morcover, in order for TCB to achieve an evenly- matched performance, needs almost 10 times labeled data than STINMatch. The overall results indicate that our STINMatch offers better results at a certain cost of consumption time.
200
400
500
1000
100
1000
10000
Labeed
Figure 4: The left insets (a) and (b) show the variation trend of the time cost and model performance with the increase training epochs. The right insets (c) and (d) show the variation trend on the best Macro F1 with increasing the labeled data size.
Table 4: Algorithm generalization experiments.
RentTheRunWar Way
Goedreads-Spoiler
Method
MacroF1
MicnoF1
MacroFF1
MicmFFI
RoBERTa
0.481
0.764
0.573
0.611
GAT-TCB
0.487
0.771
0.579
0.613
GLEM-TCB
0.501
0.789
0.585
0.624
FixMatchTCBB
0.495
0.786
0.584
0.621
SSI
0.516
0.801
0.615
0.638
ble we can see that our method still outperforms other baselines on the two public datasets, showing good generalization ability of STINMatch.
A Acknowledgements
This work is supported by the National Natural Science Foundation of China (6210603))
6 Conclusion
References
In this paper, introduce the NEKG for helping detect financial risks from commercial news. The proposed STINMatch method outperforms existing tate-oof-thearr models on news risk detection task, as well as the downstream enterprise risk evalua- tion task. Such improvements mainly come from 1) Additional NEKG topology enables article-level risk diffusion; 2) The carefully designed STIN model brings deep interactions among semantic modulc, topological module and label-correlation matrix, enhancing the sound diffusion upon NEKG. 3) The innovative semi-supervised joint learning framework enables text and graph modules to per- ceive important reciprocal advantages from each other, making both modules utilize unlabeled data more effectively. The utility of real-world applica- tions in scenarios similar to our financial risk de- tection task is substantial, ranging from multi-label document classification based on citation or so- cial networks, to multi-label product classification through e- e-Commerce platforms. We also apply the STINMatch method on two public graph-enhanced
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Table 5: Performance under the balance weight parame- ter at different scales for NEKG dataset.
MacroF1
0.05
0.878
Tianchi Yang, Linmei Hu, Chuan Shi, Houye Ji, Xiaoli Li, and Liqiang Nie. 2021b. Hgat: Heterogeneous graph attention networks for semi-supervised short text classification. TOIS 2021, 39(3):1-29.
0.1
0.882
0.5
0.889
0.883
Table 6: Distribution of risk labels for NEKG dataset.
Qian-Wen Zhang, Ximing Zhang, Zhao Yan, Ruifang Liu, Yumbo Cao, and Min-Ling Zhang. 2021. Correlation-guie guided representation for multi text classification. In Proceedings of IJCAI 2021.
Risk Label
Number of News
Bankrupicy
3376
Liquidation
2107
Business closure
272
Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, and Jian Tang. 2023. Learning on lcage text-attributed graphs variational inference. In Proceedings of ICLR 2023.
Production halts
705
Debt
Corruption
474
Dispute
5262
A Hyperparameters
Counterfeit
1187
Fraud
993
For all experiments, hyper-parametent are deter- mined by grid search, and total maximum epochs are set to the same order with early stopping to be fair. The learning rate is is 2e-4 for the text model and 5e-4 for the graph model. The batch size B is 8. B' is 7 times greater than B, which is the same to (Sohn al., 2020). The layers for graph model is 3. The confidence threshold parameter is set to 0.95. The balance weight parameter r for unsuper- vised and supervised loss set 0.5, the details can be seen Table 5. The max content length in review datasets is 512, and the max length for each news in NEKG is set to 2000. For most experimen- tal settings, we use CNN-based text encoder with pre-trained BERT embedding layer considering the length limitation and efficiency issues for fair comparison. Each text-graph iteration includes 100 text epochs and 100 graph epochs, the maximum iterations set 10. All indicators are taken from the average value of last five epochs to ensure stability.
Litigation
8277
Table 7: Risk label count news.
Risk label Count
Number of news
5855
6986
1725
344
well as other side information like reviews, ratings, product categories, etc. It contains 105,508 users, 5,850 items and 192,544 reviews. We construct bipartite graph of item nodes and review nodes. Item nodes are medium nodes similar the enter- prise nodes our enterprise graph, and the review nodes are text-attributed nodes similar to our news nodes. One item is connected to another item by an edge if they are commented by same user. We transformed the three-class fit-feedback tags and ten-class rating tags into the multi-labels.
Goodreads-Spoilers (Wan et al., 2019) dataset contains reviews from the Goodreads book review website. It contains 25,475 items, 18,892 users and 1,378,033 reviews. The constructing method of graph similar to that of RentTheRur Way. We transformed the six-class rating tags and the "has- spoiler" tag into the multi-labels.
NEKG Datasets
The distribution of each risk label among all an- notated news the details can be seen in Table 6. Besides, we also report the cireumstances of multi- label annotation of the news data in Table 7. Among all 15,000 annotated news, 5855 news associated with risk label, 6986 news are associ- ated with risk labels, and more than two thousand news have or more labels.
D Annotation Table
Finally, we provide a detailed annotation table for interpreting all symbols in Table
Review Datasets
RentTheRunWay (Misra et al., 2018) dataset con- tains self-reported fit feedback from customers as

---

Table 8: Annotation Table.
Risk Label Count
Number of news
R
label correlation matrix
K
number of all risk labels
N
vertex set for news
C
vertex set for enterprises
E
edge set of news-enterprise KG labeled news data
DL
Du
unlabeled news data
Mg
graph module
M,
text module
St
output of the text encoder in Mc
4b
hidden representation in Mr
output Mt.
1
predictions on strong augmentation data in M₁ predictions on weak augmentation data in M, node representation in M,
1
hgg
hidden representation in Mg
1g
output of Mg
1%
predictions strong augmentation data in Mg predictions weak augmentation data in Mg node-level attention weights regarding label correlation similarity-based node-level multi-head attention weights node-level attention weights in M,
3
balance weight parameter for unsupervised and supervised loss


Reviews:
[{"review_id": "pdql7A4bnD", "reviewer": "Reviewer_zFLA", "summary": "", "questions": "Question A: Are risks somewhat uniformly distributed? Or is there a particular risk that is more common? Do most news have a risk associated with them, or are they mostly neutral (highly imbalanced dataset)?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We extend our sincerest appreciation for you taking the time to review our paper and providing us with in-depth feedback. We are delighted that you recognize the novelty and potential of our idea. After careful consideration of your questions and concerns, we have formulated these feedbacks to address them.\n\n\n**Response to Question A** (*Are risks somewhat uniformly distributed? Or is there a particular risk that is more common? Do most news have a risk associated with them, or are they mostly neutral (highly imbalanced dataset)?*):\n\n- Following your suggestion, we made a table to show the risk distribution among all 15,000 annotated news, which shows that Litigation, Bankruptcy and Dispute related risks can be more popular than others.\n\n    |**Risk Label**    |**Number of news**|\n    | ----------- | ----------- |\n    |Bankruptcy|\t3376|\n    |Liquidation|2107|\n    |Business closure|\t272|\n    |Production halts|\t705|\n    |Debt|\t4097|\n    |Corruption|\t474|\n    |Dispute|\t5262|\n    |Counterfeit|\t1187|\n    |Fraud\t|993|\n    |Litigation|\t8277|\n\n- Meanwhile, we report the circumstances of multi-label annotation of the news data. Among all 15,000 annotated news, 5855 news are associated with 1 risk label, 6986 news are associated with 2 risk labels, and more than two thousand news have 3 or more labels.\n\n    |**Risk Label Count**|**Number of news**|\n    |---------------------| ----------- |\n    | 1                   |\t5855|\n    | 2                   |\t6986|\n    | 3                   |\t1725|\n    | 4                   |\t344|\n    | 5                   |\t83|\n    | 6                   |\t7|\n\nWe will supplement this information in the next version. A more detailed data description will be available on the project website.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Very thorough work is done to evaluate the effectiveness of STINMatch as well as in investigating the contribution of each component in the Ablation Study.\n\nThe creation of the NEKG dataset can be very influential in the financial domain. \n\nThe methodological contribution of STINMatch is provided quite clearly and coherently.", "reasons_to_reject": "No reason to reject. However, for the Enterprise Risk Evaluation, the decision to divide credit risk labels between high-risk and low-risk based on having more than one predicted risk label does not have strong reasoning. Since information regarding the distribution of risks (see Question A) is missing, whether or not this is a good criterion for classifying an enterprise as high or low risk is unclear. Risks such as Bankruptcy risk may pose a greater financial risk than others for example.", "questions_to_authors": "Question A: Are risks somewhat uniformly distributed? Or is there a particular risk that is more common? Do most news have a risk associated with them, or are they mostly neutral (highly imbalanced dataset)?", "paper_topic_and_main_contributions": "The paper focuses on detecting risks associated with information from news (10 risks in particular). The proposed method (STINMatch) is a semi-supervised learning framework that uses semantic information and topological association (via a News-Enterprise Knowledge Graph aka NEKG) to evaluate the propagation of news risk and enterprise risk. Along with the proposed framework, the paper also provides a NEKG dataset annotated with financial risks, as well as a new aggregation mechanism is proposed to handle multi-label propagation on text-attributed graphs.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "MZ41tGbj1j", "reviewer": "Reviewer_oisY", "summary": "", "questions": "A.The description of existing work is unclear. For instance, the reference to \"two modules\" in line 136 and \"single module\" in line 159 is ambiguous. \nB.Line 231-line 233: Why can the diffusion model provide the probability distribution of risk labels for enterprises? What prevents other methods from achieving the same result?\nC.The purpose of data enhancement is only to calculate supervised loss and unsupervised loss. Why did the authors employ both strong and weak enhancements?\nD.Calculating the similarity between different labels can also measure their associations. What are the advantages of the author's proposed association matrix R?\nE.The description of the baseline methods is unclear. Which baseline methods are considered multi-label methods? How was the conclusion in lines 508-510 derived?\nF.Comparing performance with the simplest TCB in the time cost section is not convincing.\nG.The generalization experiments on the two public datasets do not include all baseline methods. Why?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are immensely grateful for your thoughtful review of our paper, and for taking the time to provide vital feedback. Our team has carefully weighed the doubts you raised and we are providing the following response.\n\n**Response to Question A** (*Calculating the similarity between different labels can also measure their associations. What are the advantages of the author's proposed association matrix R?*):  \n- The proposed correlation matrix R exactly measures the similarity between different labels, as indicated in Line 377-380: *“we utilize the correlation matrix R to capture the internal relations among different labels to enable cross-label similarity calculation”*. More specifically, the matrix measures the similarity between the distribution of risk labels among samples. \n\n- By leveraging the proposed STINMatch method, we seek to optimize the integration of the correlation matrix R into an iterative joint learning framework in order to maximize performance and efficiency. Our approach aims to refine the calculation of the correlation matrix R, enabling the iterative joint learning framework to reach its full potential while still maintaining optimal levels of efficiency. We appreciate your question, and we will provide additional narrative in the revision to highlight this problem. \n\n\n**Response to Question B** (*\tComparing performance with the simplest TCB in the time cost section is not convincing.*):\n\n- The ”Time Cost Analysis“ serves to assess the time performance of the proposed method, which is a potential limitation of the proposed method based on joint learning. The additional time cost can be introduced by the graph-text iterative learning framework as well as the semi-supervised techniques, which involve more training rounds and data points in the learning process. To evaluate the worst case of this limitation, we compare the time cost between STINMatch and the simplest baseline, TCB, which takes the least training time among all baselines. Our results indicate that the additional time cost introduced by STINMatch is tolerable regarding the improvement in model utility (e.g., enhancing 9.5% F1).\n\n**Response to Question C** (*Line 231-line 233: Why can the diffusion model provide the probability distribution of risk labels for enterprises? What prevents other methods from achieving the same result?*):\n\n- The proposed graph module of the diffusion model is effective in propagating risk labels from news to enterprises during its training process. Unlike other baseline models, this diffusion model reflects a real-world risk diffusion process, wherein the risk labels for enterprises are randomized at the outset. Upon the diffusion model reaching convergence, the enterprise risk labels are adopted as signals for precisely quantifying risk evaluation. The proposed semantic-topological-iteration training framework and the accompanying semi-supervised learning mechanism signify an advancement in how we propagate risk labels in graph-based methods, which differs from other baselines. With our propositions, we see an increase in the effectiveness of the process. We will host additional content in the revision to address this problem.   \n\n**Response to Question D** (*The generalization experiments on the two public datasets do not include all baseline methods. Why?  *):\n\n- Thank you for this important comment. Unfortunately, because of the space limitation, we cannot host all the experiment outcomes in the paper. Here, we supplement the results for other baselines in the below table: \n\n| Method           | RentTheRunWay |         | Goodreads-Spoilers |         |\n|------------------|---------------|---------|--------------------|---------|\n|                  | MacroF1       | MicroF1 | MacroF1            | MicroF1 |\n|FastText         | 0.448         | 0.727   | 0.551              | 0.581   |\n|TextRCNN$^\\dagger$         | 0.467         | 0.748   | 0.563              | 0.601   |\n|TextRNN-attention$^\\dagger$ | 0.465         | 0.746   | 0.566              | 0.604   |\n|TextCNN$^\\dagger$          | 0.469         | 0.749   | 0.564              | 0.603   |\n|RoBERTa| 0.481         | 0.764   | 0.573              | 0.613   |\n|LCNNI$^\\dagger$            | 0.471         | 0.752   | 0.560              | 0.592   |\n|SGM              | 0.463         | 0.746   | 0.561              | 0.594   |\n|CORE$^\\dagger$             | 0.486         | 0.771   | 0.576              | 0.615   |\n|TCB-Focal-loss   | 0.476         | 0.761   | 0.565              | 0.605   |\n|TCB-DB-loss      | 0.475         | 0.760   | 0.564              | 0.605   |\n|Pseudo-TCB       | 0.474         | 0.758   | 0.567              | 0.607   |\n|MixMatch-TCB     | 0.484         | 0.766   | 0.573              | 0.612   |\n|ReMixMatch-TCB   | 0.483         | 0.764   | 0.571              | 0.610   |\n|FixMatch-TCB| 0.495         | 0.786   | 0.584              | 0.621   |\n|GCN-TCB          | 0.481         | 0.765   | 0.571              | 0.609   |\n|GraphSAGE-TCB    | 0.484         | 0.767   | 0.573              | 0.612   |\n|GAT-TCB| 0.487         | 0.771   | 0.579              | 0.613   |\n|MAGNET-TCB       | 0.485         | 0.769   | 0.574              | 0.609   |\n|LC-GAT-TCB       | 0.496         | 0.782   | 0.581              | 0.620   |\n|GLEM-TCB| 0.501         | 0.789   | 0.585              | 0.624   |\n| SSINMatch        | 0.516         | 0.801   | 0.615              | 0.638   |\n\n- If space permits, we will include the below table in the revised manuscript. Otherwise, this information will be readily accessible on the project website.\n\n\n\n\n**Response to Question E** (*The description of existing work is unclear. For instance, the reference to \"two modules\" in line 136 and \"single module\" in line 159 is ambiguous. *):\n\n- Our sincerest gratitude is extended for your detailed suggestions on this work. We will certainly incorporate your critical insights to strengthen the content. The \"two modules\" in line 136 refer to a graph module and a text module, as indicated in line 131: *‘Some recent works are combining GNNs with text classifiers to take advantage of both…”*.   \nThe “single module” in line 159 means a single text module or a single graph module.\nSince these descriptions are not direct enough, we will clarify this in the next version. We will rewrite this section. \n\n\n\n**Response to Question F** (*The purpose of data enhancement is only to calculate supervised loss and unsupervised loss. Why did the authors employ both strong and weak enhancements?  *):\n\n- Please refer to the response to *Explanation to strong and weak augmentation* in * Response to \"Reasons To Reject\"*.\n\n\n**Response to Question G** (*The description of the baseline methods is unclear. Which baseline methods are considered multi-label methods? How was the conclusion in lines 508-510 derived?*):\n\n- The *multi-label methods* in line 509 refer to Category 2 baselines in Section 5.2, which are state-of-the-art text models specialized for MLTC tasks (see L483) where MLTC refers to multi-label text classification (see L51).  \n\n- From the results in Cat2 of Table1, we can derive the conclusion in lines 508-510: “One can witness slight improvements with some multi-label methods, indicating the usefulness of correlations among labels”. We will make this paragraph clearer in the next version.\n\n\n**Response to \"Reasons To Reject\"**:\n \n\n- **Evaluation on the effectiveness of joint learning**:  \n    - We apologize for any confusion the evaluation section may have caused and seek to clarify the settings and motivation behind each comparison experiment in the revision.\n    - In Section 5.3, we evaluated the effectiveness of joint learning by comparing the results between the proposed method and the baselines in Category 2, which are semi-supervised-enhanced text classification without text-graph joint learning. We will point this out to clarify that baselines in Category 2 are involved for evaluating the improvement by text-graph joint learning.\n    - In addition, the two descriptions \"w/o unsupervised loss for $M_g$\" and \"w/o unsupervised loss for $M_t$\" in the ablation study in Section 5.4 indicate the results after removing the semi-supervised techniques from the text model and the graph model, respectively. This serves to evaluate the effectiveness of the semi-supervised component. We will rewrite this part. \n\n\n\n- **Explanation to strong and weak augmentation**:  \n   - We employ weak and strong data augmentation following the intuition of consistency regularization as described in Line 151-153: “use weakly-augmented examples to generate artificial labels and enforce consistency against strongly augmented examples.” More specifically, the goal of consistency regularization is to encourage the model to produce similar predictions for similar inputs even under different perturbations. Some previous studies, such as ReMixMatch and FixMatch, have shown the effectiveness of using both weak and strong augmentations for consistency regularization in semi-supervised learning. Similarly, we use the pseudo labels obtained from weak augmentation to fit the samples with strong augmentation, so that the model can better adapt to unseen data. The weak augmentation provides higher accuracy for the pseudo labels, while strong augmentation provides better diversity and a larger region of sample perturbation for the consistency regularization, thereby improving the performance of the semi-supervised learning. \n\n    - We will host additional narrative in the revision to address your concern and also help readers to understand the difference between strong and weak augmentations. \n\n\n- **Notation Table**: \n\n     - We appreciate your important input and concur that the addition of an annotation table would strengthen the paper and make it easier to understand. To that end, we will incorporate the following table into our revision to provide clarity for all symbols.  \n\n    | Symbol     | Description                                                   |\n    | ----------- |---------------------------------------------------------------|\n    |$\\mathbb R$| label correlation matrix                                      |\n    |$K$| \tnumber of all risk labels                                    |\n    |$N$| vertex set for news                                           |\n    |$C$| vertex set for enterprises                                    |\n    |$E$| edge set of news-enterprise KG                                |\n    |$D_L$| labeled news data                                             |\n    |$D_U$| unlabeled news data                                           |\n    |$M_g$| graph module                                                  |\n    |$M_t$| text module                                                   |\n    |$x_t$| output of the text encoder in $M_t$                           |\n    |$\\psi_t$| hidden representation in $M_t$                                |\n    |$l_t$| output of $M_t$                                               |\n    |$l_t^s$| predictions on strong augmentation data in $M_t$              |\n    |$l_t^w$| predictions on weak augmentation data in $M_t$                |\n    |$h_g$| node representation in $M_g$                                  |\n    |$\\psi_g$| hidden representation in $M_g$                                |\n    |$l_g$| output of $M_g$                                               |\n    |$l_g^s$| predictions on strong augmentation data in $M_g$              |\n    |$l_g^w$| predictions on weak augmentation data in $M_g$                |\n    |$\\beta$| node-level attention weights regarding label correlation      |\n    |$\\eta$| similarity-based node-level multi-head attention weights      |\n    |$\\alpha$| node-level attention weights in $M_g$                         |\n    |$\\gamma$| balance weight parameter for unsupervised and supervised loss |", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1.The proposed method performs well even when the labeled data size is small. \n2.It reduces dependency on labeled data, resulting in significant cost savings in terms of annotation. \n3.The method achieves a 4% improvement in Macro-F1 for the news risk detection task.", "reasons_to_reject": "1.The writing of the paper is not satisfactory, and has numerous and chaotic symbols in the text. A symbol table and an algorithmic table would make the paper clearer.\n2.The rationale behind the use of weak augmentation and strong augmentation is not thoroughly explained.\n3.The effectiveness of joint learning has not been evaluated.", "questions_to_authors": "A.The description of existing work is unclear. For instance, the reference to \"two modules\" in line 136 and \"single module\" in line 159 is ambiguous. \nB.Line 231-line 233: Why can the diffusion model provide the probability distribution of risk labels for enterprises? What prevents other methods from achieving the same result?\nC.The purpose of data enhancement is only to calculate supervised loss and unsupervised loss. Why did the authors employ both strong and weak enhancements?\nD.Calculating the similarity between different labels can also measure their associations. What are the advantages of the author's proposed association matrix R?\nE.The description of the baseline methods is unclear. Which baseline methods are considered multi-label methods? How was the conclusion in lines 508-510 derived?\nF.Comparing performance with the simplest TCB in the time cost section is not convincing.\nG.The generalization experiments on the two public datasets do not include all baseline methods. Why?", "paper_topic_and_main_contributions": "This paper proposes improvements for utilizing NEKG in financial risk detection. First, it proposes a semi-supervised text-graph joint learning framework that fully exploits semantic information and topological associations. Second, it introduces a content-label-topology aggregation mechanism to address multi-label diffusion issues on text-attributed graphs. Third, it releases a NEKG dataset annotated with multiple financial risks. The proposed method is evaluated on three datasets and achieves higher accuracy and F1 scores compared to baseline methods.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "None", "Typos_Grammar_Style_and_Presentation_Improvements": "None"}, "extra_text": {}}, {"review_id": "whbq1ACyu5", "reviewer": "Reviewer_BEjE", "summary": "", "questions": "- A. In L167, the labels Y are in R^k. I wonder why the label values scatter across the real number set. And could you please give an example whose Y has a certain dimension of a very large value? In L179, the news piece in the NEKG has K binary risk labels. This is inconsistent with the former formulation.\n- B. Why are the information providers anonymous? And how could we know how authoritative the information is without knowing about the source?\n- C. Have you ever considered what if a piece of news indicates no risk of a related company? Is this in the research scope? If not, what pre-processing steps are required, and how to do them?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful feedback. We are committed to improving our research study, so we eagerly look forward to incorporating your suggestions for further refinement.  \n\n**Response to Question A**(*Have you ever considered what if a piece of news indicates no risk of a related company? Is this in the research scope? If not, what pre-processing steps are required, and how to do them?  *): \n\n- The scope of our research encompasses companies with varying levels of risk, including the ones with “no risk”. As outlined in Section 5.3 (enterprise risk evaluation), our dataset includes those companies that exhibit low risk, including those with no risk, as illustrated in Line 532. In response to your question, we will enhance our revision with an expanded narrative to further address this issue. \n\n\n**Response to Question B**(*Why are the information providers anonymous? And how could we know how authoritative the information is without knowing about the source?    *):\n\n- We anonymize the information providers to meet the anonymous requirement during the double-blind review period. We will provide detailed data information in the revision if the paper is accepted. This enterprise information is sourced from a government-backed open data provider,  and it enables the designed web-crawler to collect the corresponding news for annotations and evaluations. We will provide explanations in the final version.  \n\n**Response to Question C**(*In L167, the labels Y are in R^k. I wonder why the label values scatter across the real number set. And could you please give an example whose Y has a certain dimension of a very large value? In L179, the news piece in the NEKG has K binary risk labels. This is inconsistent with the former formulation.  *):\n\n- We would like to clarify that in L167, $R^K$ represents $K$-dimensional vector space with real numbers and $y\\in R^K$ represents that $y$ is a $K$-dimensional vector. We will make this clearer in the next version.\n\n**Response to \"Typos Grammar Style And Presentation Improvements\"**:  \n\n- We are appreciative of the thoughtful comments, and are eager to address your concerns in the forthcoming revision of our paper. Your thoughtful feedback is essential to our continued growth. Please find our responses in the following content.\n\n**Response to \"Reasons To Reject\"**:\n- **Difference with previous methods**:   \n    - Our work advocates for an innovative approach to financial risk detection that integrates label correlation effectively into the text-graph iterative learning process. \n    - The integration of label correlation into the iterative learning process offers a mutual benefit, significantly improving the overall performance of the system. On the one hand, the label correlation, serving as domain-specific knowledge, plays an important role to guide the label diffusion for both text and graph modules in each iteration; on the other hand, the predictions of the successively enhanced model help involve more previously unlabeled samples into the calculation of label correlation, resulting in a more generalized label correlation in each iteration round.\n    - We will rewrite this statement to make the model novelty more understandable. \n\n- **Real-world requirement of the proposed technique**: \n    - Thanks for your comments. This paper proposes leveraging a text-graph joint learning framework equipped with semi-supervised learning techniques to develop an effective multi-label financial risk detection system. To tackle the expensive data annotation cost which is traditionally required for such applications, we will make the annotation data publicly available.  \n\n    - The utility of real-world applications in scenarios similar to ours is substantial, ranging from multi-label document classification based on citation or social networks, to multi-label product classification through eCommerce platforms. Nevertheless, obtaining labels for these datasets can require a lot of resources, particularly if the tasks are linked to domains such as medicine or technology that require specialized knowledge. Traditional text-graph joint learning approaches are ineffective in tackling this difficulty due to their lack of semi-supervised learning techniques.\n    - Following your suggestion, we will host additional narrative to address this problem.  \n\n- **Basic information about the data source**:\n    - Please refer to the response to * Response to Question B\"*.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The task of financial risk detection is of great real-world application value.\n2. The experiment part is diverse and it shows the superiority of the proposed STINMatch framework.\n3. The overall writing is good. The notations, figures, and organization are carefully designed.\n4. The authors acknowledge the increasing computation cost of the proposed method in the limitation section with a reasonable and clear analysis, which is seldom across my batch of submissions to be reviewed.", "reasons_to_reject": "1. This motivation is illustrated by only listing existing works and the proposed one, lacking an analysis of the key challenges.\n   - First, in L060-061, the authors say that the proposed method is unlike previous graph neural network (GNN) methods for text analysis. However, readers could not know how these previous methods do it and what is different if a method should focus on multi-label-correlation guided text-graph joint learning. Is the difference in multi-labeling, correlation, guidance, or text-graph joint learning?\n   - Second, in L075-077, the case is similar. The authors state that few studies have been carried out on the semi-supervised integration for text-graph joint learning framework. I wonder what is different and challenging again because the situation that few studies consider semi-supervised integration for this framework is caused by non-technical factors (e.g., no real-world requirement, existing techniques are enough).\n2. Some basic information about the data source is not disclosed. We know little about how reliable the new NEKG dataset is.\n3. The task definition is confusing. Intuitively, a task named risk detection is to detect risky objects, i.e., to say whether a sample is risky or not (binary). According to Section 3, financial risk classification might be a more proper name for this task. Or a no-risk label should be included in the label space.\n", "questions_to_authors": "- A. In L167, the labels Y are in R^k. I wonder why the label values scatter across the real number set. And could you please give an example whose Y has a certain dimension of a very large value? In L179, the news piece in the NEKG has K binary risk labels. This is inconsistent with the former formulation.\n- B. Why are the information providers anonymous? And how could we know how authoritative the information is without knowing about the source?\n- C. Have you ever considered what if a piece of news indicates no risk of a related company? Is this in the research scope? If not, what pre-processing steps are required, and how to do them?", "paper_topic_and_main_contributions": "This paper focuses on the financial risk detection task, which is to predict the risk label distribution of the target news piece. The proposed framework, STINMatch, is a text-graph joint modeling framework to exploit both the text information and the graph (news-company) information for better risk label diffusion. Experiments on a newly constructed dataset and two public datasets show that the proposed STINMatch outperforms baselines in four categories.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "I am not an expert on this task and have no more references to provide."}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "Reference issues:\n1. If possible, please add URLs or DOIs for all the entries. This would make it easier for readers using computers to redirect to the original papers.\n2. The paper \"Multi-label text classification using attention-based graph neural network\" has been published at ICAART 2020. Please do not cite its preprint version.\n\nPresentation issues (maybe it would be better to copy the main text to grammar-checking tools before submitting it):\n- L041: utilizing -> utilize\n- l051: The abbreviation NLP is unnecessary.\n- L091: a -> an\n- L239: Fig.2 -> Fig. 2\n- L609: section -> Section\n- L594: 10,00 -> 1,000\n- L655: A ending period is missing.\n- Table 1: Seems unnecessary to use \"Cat\" as a short name for \"Category\". The space is enough."}}, {"review_id": "6dbdgEYLWG", "reviewer": "Reviewer_CCXt", "summary": "", "questions": "a)   In section 3, the authors denote two edges in NEKG, i.e., news-news and news-enterprise E = {e|e = (p, q), p ∈ N, q ∈ N}∪ {e|e = (p, q), p ∈ N, q ∈C}. However, there is no news-news edge in the following sections but the enterprise-enterprise edge, for example, in Figure 1. Which one is correct?\n\nb) The model framework in Figure 2 is hard to understand. Could you provide a necessary description of important symbols, such as M_t and M_g?\n", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are grateful for your insightful review comments, and we heartily welcome the opportunity to further articulate our position in explanation to the queries you raised.  \n\n**Response to Question A**(*In section 3, the authors denote two edges in NEKG, i.e., news-news and news-enterprise E = {e|e = (p, q), p ∈ N, q ∈ N}∪ {e|e = (p, q), p ∈ N, q ∈C}. However, there is no news-news edge in the following sections but the enterprise-enterprise edge, for example, in Figure 1. Which one is correct?  *):\n\n- Thank you for pointing out the typo. The edge set should be E = {e|e = (p, q), p ∈ C, q ∈ C}∪ {e|e = (p, q), p ∈ N, q ∈C}.\n\n**Response to Question B**(*The model framework in Figure 2 is hard to understand. Could you provide a necessary description of important symbols, such as M_t and M_g?  *):  \n- Thank you for the suggestions. We provide a notation table to describe all important symbols. Please refer to the response to  *Explanation of symbols* in *Response to \"Reasons To Reject\"*.\n\n\n**Response to \"Reasons To Reject\"**:\n - **Influence of important hyperparameters**  \n    - Thanks for your suggestions. Below we show the model performance under the balance weight parameter at different scales to explain our selection:\n\n    |**γ value**|**MacroF1**|\n    |-------------|--------------|\n    | 0.05\t       | 0.878        | \n    | 0.1\t        | 0.882        | \n    | 0.5\t        | 0.889        | \n    | 1\t          | 0.883        |\n    - We will supplement this information in the next version.\n\n- **Distribution of risk label**\n    - Thank you for pointing this out. Below we show the distribution of each risk label among all 15,000 annotated news.\n\n    |**Risk Label**    |**Number of news**|\n    | ----------- | ----------- |\n    |Bankruptcy|\t3376|\n    |Liquidation|2107|\n    |Business closure|\t272|\n    |Production halts|\t705|\n    |Debt|\t4097|\n    |Corruption|\t474|\n    |Dispute|\t5262|\n    |Counterfeit|\t1187|\n    |Fraud\t|993|\n    |Litigation|\t8277|\n\n\n    - Besides, we report the circumstances of multi-label annotate for these news. We will supplement this information in the next version.\n    \n|**Risk Label Count**|**Number of news**|\n|-----------------------| ----------- |\n| 1                     |\t5855|\n| 2                     |\t6986|\n| 3                     |\t1725|\n| 4                     |\t344|\n| 5                     |\t83|\n\n - **Explanation of symbols**\n\n    Following your suggestions, we provide a detailed annotation table for interpreting all symbols. We will supplement this information in the next version.\n\n    | Symbol     | Description                                                   |\n    | ----------- |---------------------------------------------------------------|\n    |$\\mathbb R$| label correlation matrix                                      |\n    |$K$| \tnumber of all risk labels                                    |\n    |$N$| vertex set for news                                           |\n    |$C$| vertex set for enterprises                                    |\n    |$E$| edge set of news-enterprise KG                                |\n    |$D_L$| labeled news data                                             |\n    |$D_U$| unlabeled news data                                           |\n    |$M_g$| graph module                                                  |\n    |$M_t$| text module                                                   |\n    |$x_t$| output of the text encoder in $M_t$                           |\n    |$\\psi_t$| hidden representation in $M_t$                                |\n    |$l_t$| output of $M_t$                                               |\n    |$l_t^s$| predictions on strong augmentation data in $M_t$              |\n    |$l_t^w$| predictions on weak augmentation data in $M_t$                |\n    |$h_g$| node representation in $M_g$                                  |\n    |$\\psi_g$| hidden representation in $M_g$                                |\n    |$l_g$| output of $M_g$                                               |\n    |$l_g^s$| predictions on strong augmentation data in $M_g$              |\n    |$l_g^w$| predictions on weak augmentation data in $M_g$                |\n    |$\\beta$| node-level attention weights regarding label correlation      |\n    |$\\eta$| similarity-based node-level multi-head attention weights      |\n    |$\\alpha$| node-level attention weights in $M_g$                         |\n    |$\\gamma$| balance weight parameter for unsupervised and supervised loss |", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "a)\tThis paper proposes a new financial risk detection method that could not only jointly leverage semantics of news and topological correlations to conduct better risk diffusion estimation but also could alleviate the limited annotation data problem by semi-supervised learning.\n\nb)\tThis paper construct and release a new dataset NEKG with multiple financial risks, which could be helpful to other researchers.\n\nc)\tExtensive experiments on the constructed NEKG dataset and two public datasets show the effectiveness of this paper.\n", "reasons_to_reject": "a)\tThis paper doesn’t analyze the influence of important hyperparameters, such as the balance weight parameter γ for unsupervised and supervised loss.\n\nb)\tThis paper doesn’t introduce the risk label distribution in datasets, which is important to understand the experimental results.\n\nc)\tThe model framework in Figure 2 is hard to understand. There should be a necessary description of important symbols.\n\nd)\tThere are still some typos. In section 3, the authors denote two edges in NEKG, i.e., news-news and news-enterprise E = {e|e = (p, q), p ∈ N, q ∈ N}∪ {e|e = (p, q), p ∈ N, q ∈C}. However, there is no news-news edge in the following sections but the enterprise-enterprise edge, for example, in Figure 1.\n\ne)\tSome symbols are used but not defined. For example, K is not clearly introduced in the Semi-supervised Risk Diffusion module.\n", "questions_to_authors": "a)   In section 3, the authors denote two edges in NEKG, i.e., news-news and news-enterprise E = {e|e = (p, q), p ∈ N, q ∈ N}∪ {e|e = (p, q), p ∈ N, q ∈C}. However, there is no news-news edge in the following sections but the enterprise-enterprise edge, for example, in Figure 1. Which one is correct?\n\nb) The model framework in Figure 2 is hard to understand. Could you provide a necessary description of important symbols, such as M_t and M_g?\n", "paper_topic_and_main_contributions": "This paper proposes a novel semi-supervised method to fully leverage commercial news for automated financial risk. Specifically, it designs a semantic-topological iteration network (STIN) for multi-label risk diffusion, which mainly contains a content-label-topology aggregation mechanism to capture the interaction of STIN. Then, it integrates a semi-supervised learning method for a text-graph joint learning framework to address the limited annotation data of financial news. In addition, this paper releases a new dataset news-enterprise knowledge graph (NEKG) for multi-label financial risks. Results on real-world datasets show that this method achieves better performance than baselines.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "N/A", "Justification_for_Ethical_Concerns": "N/A"}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "a)\tThere are still some typos. In section 3, the authors denote two edges in NEKG, i.e., news-news and news- enterprise E = {e|e = (p, q), p ∈ N, q ∈ N}∪ {e|e = (p, q), p ∈ N, q ∈C}. However, there is no news-news edge in the following sections but the enterprise- enterprise edge, for example, in Figure 1.\n\nb)\tSome symbols are used but not defined. For example, K is not clearly introduced in the Semi-supervised Risk Diffusion module.\n"}}]

Author rebuttal:

Meta-review: The proposed method (STINMatch) is a semi-supervised learning framework that uses semantic information and topological association (via a News-Enterprise Knowledge Graph aka NEKG) to evaluate the propagation of news risk and enterprise risk.

This approach is really interesting although some concerns are mainly related:
1) The task definition is not properly defined; specifically more relevant definitions may support the readers in understanding the task
2) Some basic information about the data source is not disclosed.
3) Key challenges need to be more properly defined
4) More clarifications about the framework need to be provided (also improving Figure 1).
Decision: Accept-Main