OpenRaiser/Intern-Atlas huggingface dataset:
GAP: A Grammar and Position-Aware Framework for Efficient Recognition of Multi-Line Mathematical Formulas
Authors: Zhe Yang, Qi Liu, Kai Zhang, Shiwei Tong, Enhong Chen
Venue: WSDM
Year: 2024

Formula recognition endeavors to automatically identify mathematical formulas from images. Currently, the Encoder-Decoder model has significantly advanced the translation from image to corresponding formula markups. Nonetheless, previous research primarily concentrated on single-line formula recognition, ignoring the recognition of multi-line formulas, which presents additional challenges such as more stringent grammatical restrictions and two- dimensional positions. In this work, we present GAP (Grammar And Position-Aware formula recognition), a comprehensive framework designed to tackle the challenges in multi-line mathematical formula recognition. First, to overcome the limitations imposed by grammar, we design a novel Grammar Aware Contrastive Learning (GACL) module, integrating complex grammar rules into the transcription model through a contrastive learning mechanism. Furthermore, primitive contrastive learning lacks clear directions for comprehending grammar rules and can lead to unstable convergence or prolonged training cycles. To enhance training efficiency, we propose Rank-Based Sampling (RBS) specialized for multi-line formulas, which guides the learning process by the importance ranking of different grammar errors. Finally, spatial location information is critical considering the two-dimensional nature of multi-line formulas. To aid the model in keeping track of that global information, we introduced a Visual Coverage (VC) mechanism that incorporates historical attention information into the image features via a parameter-free way. To validate the effectiveness of our GAP framework, we construct a new dataset Multi-Line containing 12,002 multi-line formulas and conduct extensive experiments to show the efficacy of our GAP framework in capturing grammatical rules, enhancing recognition accuracy, and enhancing training efficiency. Codes and datasets are available at https://github.com/Sinon02/GAP.
COTER: Conditional Optimal Transport meets Table Retrieval
Authors: Xun Yao, Zhixin Zhang, Xinrong Hu, Jie (Jack) Yang, Yi Guo, Daniel (Dianliang) Zhu
Venue: WSDM
Year: 2024

Ad hoc table retrieval refers to the task of performing semantic matching between given queries and candidate tables. In recent years, the approach to addressing this retrieval task has undergone significant shifts, transitioning from utilizing hand-crafted features to leveraging the power of Pre-trained Language Models (PLMs). However, key challenges arise when candidate tables contain shared items, and/or queries may refer to only a subset of table items rather than the entire one. Existing models often struggle to distinguish the most informative items and fail to accurately identify the relevant items required to match with the query. To bridge this gap, we propose C onditional O ptimal T ransport based table retrievER (COTER). The proposed algorithm is characterized by simplifying candidate tables, where the semantic meaning of one or several words (from the original table) is enabled to be effectively "transported'' to individual words (from the simplified table), under the prior condition of the query. COTER achieves two essential goals simultaneously: minimizing the semantic loss during the table simplification and ensuring that retained items from simplified tables effectively match the given query. Importantly, the theoretical foundation of COTER empowers it to adapt dynamically to different queries and enhances the overall performance of the table retrieval. Experiments on two popular Web-table retrieval benchmarks show that COTER can effectively identify informative table items without sacrificing retrieval accuracy. This leads to the new state-of-the-art with substantial gains of up to 0.48 absolute Mean Average Precision (MAP) points, compared to the previously reported best result.
Lessons Learnt from Building Friend Recommendation Systems
Authors: Jun Yu
Venue: WSDM
Year: 2024

Friend recommendation systems in online social networks such as Snapchat help users find friends and build meaningful connections, leading to heightened user engagement and retention. While friend recommendation systems follow the classical recommendation system paradigm that consists of retrieval and ranking, they pose distinctive challenges different from item recommendation systems (e.g. Youtube videos, Amazon products, Netflix movies), and require special considerations in building one. In this paper, we elucidate the unique challenges encountered and share invaluable insights from developing the friend recommendation system for hundreds of millions of users on Snapchat.
PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization
Authors: Ziqi Yuan, Haoyi Zhou, Tianyu Chen, Jianxin Li
Venue: WSDM
Year: 2024

A multitude of toxic online behaviors, ranging from network attacks to anonymous traffic and spam, have severely disrupted the smooth operation of networks. Due to the inherent sender-receiver nature of network behaviors, graph-based frameworks are commonly used for detecting anomalous behaviors. However, in real-world scenarios, the boundary between normal and anomalous behaviors tends to be ambiguous. The local heterophily of graphs interferes with the detection, and existing methods based on nodes or edges introduce unwanted noise into representation results, thereby impacting the effectiveness of detection. To address these issues, we propose PhoGAD, a graph-based anomaly detection framework. PhoGAD leverages persistent homology optimization to clarify behavioral boundaries. Building upon this, the weights of adjacent edges are designed to mitigate the effects of local heterophily. Subsequently, to tackle the noise problem, we conduct a formal analysis and propose a disentangled representation-based explicit embedding method, ultimately achieving anomaly behavior detection. Experiments on intrusion, traffic, and spam datasets verify that PhoGAD has surpassed the performance of state-of-the-art (SOTA) frameworks in detection efficacy. Notably, PhoGAD demonstrates robust detection even with diminished anomaly proportions, highlighting its applicability to real-world scenarios. The analysis of persistent homology demonstrates its effectiveness in capturing the topological structure formed by normal edge features. Additionally, ablation experiments validate the effectiveness of the innovative mechanisms integrated within PhoGAD.
Automatic Extraction of Patterns in Digital News Articles of Femicides occurred in Mexico by Text Mining Techniques
Authors: Jonathan Zárate-Cartas, Alejandro Molina-Villegas
Venue: WSDM
Year: 2024

Femicides in Mexico are a severe social problem that, according to recent research, is increasing [7]. Although it is true that Secretariado Ejecutivo del Sistema Nacional de Seguridad Pública (SESNSP) periodically publishes information on the incidence of femicides at the national level, this information is limited to counting femicides by states in the country [5]. Given this scenario, unstructured data related to femicides in Mexico was the best source of information for this research. We analyzed 5,000 texts from digital newspaper articles from the most important newspapers in Mexico. These articles were collected by different activists during the period from 2016 to 2020.
Multi-Granular Text Classification with Minimal Supervision
Authors: Yunyi Zhang
Venue: WSDM
Year: 2024

Our society has been immersed with massive unstructured text data, posing great challenges for people to fetch needed data, digest critical information, and derive actionable knowledge. Such needs necessitate the development of text classification which is a fundamental task towards structuring the unstructured web data. Existing methods either require heavy human annotation or work only with limited scope (e.g., classification into only a small number of classes), far off from the real needs. Recently developed deep learning and pre-trained language models boost our research substantially, but many problems still remain. Therefore, we propose to develop a minimally-supervised approach to structure massive text into a multi-granularity text space. We explore the following four subtasks: (1) weak supervision enrichment, (2) PLM-enhanced weakly-supervised text classification, (3) empowering fine-grained text classification with enriched taxonomy, (4) joint classification of multi-granular text units.
A Scalable Open-Source System for Segmenting Urban Areas with Road Networks
Authors: Ming Zhang, Yanyan Li, Jianguo Duan, Jizhou Huang, Jingbo Zhou
Venue: WSDM
Year: 2024

\beginabstract Segmenting an urban area into regions is fundamentally important for many spatio-temporal applications. The traditional grid-based method offers a simple solution as it divides the city map into equal-sized grids, but it fails to preserve semantic information about the original urban structure. Several studies apply the road network to cut the metropolitan area into meaningful blocks. However, existing works do not achieve good scalability, and there is no public system provided so far. To address those problems, we build\mysystem, the first scalable vector-based system which generates reasonable regions using all levels of the road network. We conduct an evaluation to prove the efficiency and effectiveness of our system. We also publish our system as a Python library through Python Package Index (PyPI), and demonstrate its utility using real public datasets in this paper. The source code and useful instructions can be found on https://github.com/PaddlePaddle/PaddleSpatial/tree/main/paddlespatial/tools/genregion. \endabstract
IncMSR: An Incremental Learning Approach for Multi-Scenario Recommendation
Authors: Kexin Zhang, Yichao Wang, Xiu Li, Ruiming Tang, Rui Zhang
Venue: WSDM
Year: 2024

For better performance and less resource consumption, multi-scenario recommendation (MSR) is proposed to train a unified model to serve all scenarios by leveraging data from multiple scenarios. Current works in MSR focus on designing effective networks for better information transfer among different scenarios. However, they omit two important issues when applying MSR models in industrial situations. The first is the efficiency problem brought by mixed data, which delays the update of models and further leads to performance degradation. The second is that MSR models are insensitive to the changes of distribution over time, resulting in suboptimal effectiveness in the incoming data. In this paper, we propose an incremental learning approach for MSR (IncMSR), which can not only improve the training efficiency but also perceive changes in distribution over time. Specifically, we first quantify the pair-wise distance between representations from scenario, time and time-scenario dimensions respectively. Then, we decompose the MSR model into scenario-shared and scenario-specific parts and apply fine-grained constraints on the distances quantified with respect to the two different parts. Finally, all constraints are fused in an elegant way using a metric learning framework as a supplementary penalty term to the original MSR loss function. Offline experiments on two real-world datasets are conducted to demonstrate the superiority and compatibility of our proposed approach.
AAGenRec: A Novel Approach for Mitigating Inter-task Interference in Multi-task Optimization of Sequential Behavior Modeling
Authors: Jiawei Zhang, Shimin Yang, Liang Shen
Venue: WSDM
Year: 2024

Multi-task optimization is an emerging research field in recommender systems that aims to enhance the recommendation performance across multiple tasks. Various methodologies have been introduced to address aspects like balancing task weights, handling gradient conflicts, and achieving Pareto optimality. These approaches have shown promise in specific contexts, but are not well-suited for real-world scenarios that involve user sequential behaviors. To address this gap, we present AAGenRec, a novel and effective solution for sequential behavior modeling within multi-task recommender systems, inspired by concepts from acoustic attenuation and genetic differences. Specifically, AAGenRec leverages an established genetic distance method to quantify the dissimilarity between tasks, then introduces an impact attenuation mechanism to mitigate the uncertain task interference in multi-task optimization. Extensive experiments conducted on public e-commerce datasets demonstrate the effectiveness of AAGenRec.
Wildfire: A Twitter Social Sensing Platform for Layperson
Authors: Zeyu Zhang, Zhengyuan Zhu, Haiqi Zhang, Foram Patel, Josue Caraballo, Patrick Hennecke, Chengkai Li
Venue: WSDM
Year: 2024

We present Wildfire, an innovative social sensing platform designed for laypersons. The goal is to support users in conducting social sensing tasks using Twitter data without programming and data analytics skills. Existing open-source and commercial social sensing tools only support data collection using simple keyword-based or account-based search. On the contrary, Wildfire employs a heuristic graph exploration method to selectively expand the collected tweet-account graph in order to further retrieve more task-relevant tweets and accounts. This approach allows for the collection of data to support complex social sensing tasks that cannot be met with a simple keyword search. In addition, Wildfire provides a range of analytic tools, such as text classification, topic generation, and entity recognition, which can be crucial for tasks such as trend analysis. The platform also provides a web-based user interface for creating and monitoring tasks, exploring collected data, and performing analytics.
RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis
Authors: Xusheng Zhao, Hao Peng, Qiong Dai, Xu Bai, Huailiang Peng, Yanbing Liu, Qinglang Guo, Philip S. Yu
Venue: WSDM
Year: 2024

Aspect-based sentiment analysis (ABSA) is dedicated to forecasting the sentiment polarity of aspect terms within sentences. Employing graph neural networks to capture structural patterns from syntactic dependency parsing has been confirmed as an effective approach for boosting ABSA. In most works, the topology of dependency trees or dependency-based attention coefficients is often loosely regarded as edges between aspects and opinions, which can result in insufficient and ambiguous syntactic utilization. To address these problems, we propose a new reinforced dependency graph convolutional network (RDGCN) that improves the importance calculation of dependencies in both distance and type views. Initially, we propose an importance calculation criterion for the minimum distances over dependency trees. Under the criterion, we design a distance-importance function that leverages reinforcement learning for weight distribution search and dissimilarity control. Since dependency types often do not have explicit syntax like tree distances, we use global attention and mask mechanisms to design type-importance functions. Finally, we merge these weights and implement feature aggregation and classification. Comprehensive experiments show the effectiveness of the criterion and importance functions. RDGCN yields excellent analysis results.
Interpretable Imitation Learning with Dynamic Causal Relations
Authors: Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen
Venue: WSDM
Year: 2024

Imitation learning, which learns agent policy by mimicking expert demonstration, has shown promising results in many applications such as medical treatment regimes and self-driving vehicles. However, it remains a difficult task to interpret control policies learned by the agent. Difficulties mainly come from two aspects: 1) agents in imitation learning are usually implemented as deep neural networks, which are black-box models and lack interpretability; 2) the latent causal mechanism behind agents' decisions may vary along the trajectory, rather than staying static throughout time steps. To increase transparency and offer better interpretability of the neural agent, we propose to expose its captured knowledge in the form of a directed acyclic causal graph, with nodes being action and state variables and edges denoting the causal relations behind predictions. Furthermore, we design this causal discovery process to be state-dependent, enabling it to model the dynamics in latent causal graphs. Concretely, we conduct causal discovery from the perspective of Granger causality and propose a self-explainable imitation learning framework, CAIL. The proposed framework is composed of three parts: a dynamic causal discovery module, a causality encoding module, and a prediction module, and is trained in an end-to-end manner. After the model is learned, we can obtain causal relations among states and action variables behind its decisions, exposing policies learned by it. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of the proposed CAIL in learning the dynamic causal graphs for understanding the decision-making of imitation learning meanwhile maintaining high prediction accuracy.
CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting
Authors: Zhengyang Zhou, Jiahao Shi, Hongbo Zhang, Qiongyu Chen, Xu Wang, Hongyang Chen, Yang Wang
Venue: WSDM
Year: 2024

Spatiotemporal traffic forecasting plays a critical role in intelligent transportation systems, which empowers diverse urban services. Existing traffic forecasting frameworks usually devise various learning strategies to capture spatiotemporal correlations from the perspective of volume itself. However, we argue that previous traffic predictions are still unreliable due to two aspects. First, the influences of context factor-wise interactions on dynamic region-wise correlations are under exploitation. Second, the dynamics induce the credibility issue of forecasting that has not been well-explored. In this paper, we exploit the informative traffic-related context factors to jointly tackle the dynamic regional heterogeneity and explain the stochasticity, towards a credible uncertainty-aware traffic forecasting. Specifically, to internalize the dynamic contextual influences into learning process, we design a context-cross relational embedding to capture interactions between each context, and generate virtual graph topology to dynamically relate pairwise regions with context embedding. To quantify the prediction credibility, we attribute data-side aleatoric uncertainty to contexts and re-utilize them for aleatoric uncertainty quantification. Then we couple a dual-pipeline learning with the same objective to produce the discrepancy of model outputs and quantify model-side epistemic uncertainty. These two uncertainties are fed through a spatiotemporal network for extracting uncertainty evolution patterns. Finally, comprehensive experiments and model deployments have corroborated the credibility of our framework.
Dance with Labels: Dual-Heterogeneous Label Graph Interaction for Multi-intent Spoken Language Understanding
Authors: Zhihong Zhu, Xuxin Cheng, Hongxiang Li, Yaowei Li, Yuexian Zou
Venue: WSDM
Year: 2024

Multi-intent spoken language understanding (SLU) has garnered increasing attention since it can handle complex utterances expressing multiple intents in real-world scenarios. However, existing joint models are disturbed by label statistical frequencies, or adopt homogeneous graphs to capture interactions between the different types (e.g., intent and slot) of label nodes, thereby limiting the performance. To overcome these limitations, we propose Dual Heterogeneous Graph Label Interaction for multi-intent SLU, named DHLG. Concretely, we propose a global static heterogeneous label graph interaction layer to model both intra- and inter-label statistical dependencies across the entire training corpus. Based on this, we introduce a local dynamic heterogeneous label graph layer to further facilitate adaptive interactions between intents and slots for each utterance. Extensive experiments and analyses on two widely-used benchmark datasets demonstrate the superiority of our proposed DHLG over state-of-the-art methods.
The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation
Authors: Yuchang Zhu, Jintang Li, Liang Chen, Zibin Zheng
Venue: WSDM
Year: 2024

Graph neural networks (GNNs) are being increasingly used in many high-stakes tasks, and as a result, there is growing attention on their fairness recently. GNNs have been shown to be unfair as they tend to make discriminatory decisions toward certain demographic groups, divided by sensitive attributes such as gender and race. While recent works have been devoted to improving their fairness performance, they often require accessible demographic information. This greatly limits their applicability in real-world scenarios due to legal restrictions. To address this problem, we present a demographic-agnostic method to learn fair GNNs via knowledge distillation, namely FairGKD. Our work is motivated by the empirical observation that training GNNs on partial data (i.e., only node attributes or topology data) can improve their fairness, albeit at the cost of utility. To make a balanced trade-off between fairness and utility performance, we employ a set of fairness experts (i.e., GNNs trained on different partial data) to construct the synthetic teacher, which distills fairer and informative knowledge to guide the learning of the GNN student. Experiments on several benchmark datasets demonstrate that FairGKD, which does not require access to demographic information, significantly improves the fairness of GNNs by a large margin while maintaining their utility.\footnoteOur code is available via: \code.
Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices
Authors: Jing Zhu, Yuhang Zhou, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra
Venue: WSDM
Year: 2024

While Graph Neural Networks (GNNs) are remarkably successful in a variety of high-impact applications, we demonstrate that, in link prediction, the common practices of including the edges being predicted in the graph at training and/or test have outsized impact on the performance of low-degree nodes. We theoretically and empirically investigate how these practices impact node-level performance across different degrees. Specifically, we explore three issues that arise: (I1) overfitting; (I2) distribution shift; and (I3) implicit test leakage. The former two issues lead to poor generalizability to the test data, while the latter leads to overestimation of the model's performance and directly impacts the deployment of GNNs. To address these issues in a systematic way, we introduce an effective and efficient GNN training framework, SpotTarget, which leverages our insight on low-degree nodes: (1) at training time, it excludes a (training) edge to be predicted if it is incident to at least one low-degree node; and (2) at test time, it excludes all test edges to be predicted (thus, mimicking real scenarios of using GNNs, where the test data is not included in the graph). SpotTarget helps researchers and practitioners adhere to best practices for learning from graph data, which are frequently overlooked even by the most widely-used frameworks. Our experiments on various real-world datasets show that SpotTarget makes GNNs up to 15× more accurate in sparse graphs, and significantly improves their performance for low-degree nodes in dense graphs.