OpenRaiser/Intern-Atlas huggingface dataset:
Budget-constrained Truss Maximization over Large Graphs: A Component-based Approach
Authors: Xin Sun, Xin Huang, Zitan Sun, Di Jin
Venue: CIKM
Year: 2021

Cohesive substructure identification is one fundamental task of graph analytics. Recently, a useful problem of dense subgraph maximization has attracted significant attentions, which aims at enlarging a dense subgraph pattern using a few new edge insertions, e.g., k-core maximization. As a more cohesive subgraph of k-core, k-truss requires that each edge has at least k-2 triangles within this subgraph. However, the problem of k-truss maximization has not been studied yet. In this paper, we motivate and formulate a new problem of budget-constrained k-truss maximization. Given a budget of b edges and an integer k≥2, the problem is to find and insert b new edges into a graph G such that the resulted k-truss of G is maximized. We theoretically prove the NP-hardness of k-truss maximization problem. To efficiently tackle it, we analyze non-submodular property of k-truss newcomers function and develop non-conventional heuristic strategies for edge insertions. We first identify high-quality candidate edges with regard to (k-1)-light subgraphs and propose a greedy algorithm using per-edge insertion. Besides further improving the efficiency by pruning disqualified candidate edges, we finally develop a component-based dynamic programming algorithm for enlarging k-truss mostly, which makes a balance of budget assignment and inserts multiple edges simultaneously into all (k-1)-light components. Extensive experiments on nine real-world graphs demonstrate the efficiency and effectiveness of our proposed methods.
K-AID: Enhancing Pre-trained Language Models with Domain Knowledge for Question Answering
Authors: Fu Sun, Feng-Lin Li, Ruize Wang, Qianglong Chen, Xingyi Cheng, Ji Zhang
Venue: CIKM
Year: 2021

Knowledge enhanced pre-trained language models (K-PLMs) are shown to be effective for many public tasks in the literature, but few of them have been successfully applied in practice. To address this problem, we propose K-AID, a systematic approach that includes a low-cost knowledge acquisition process for acquiring domain knowledge, an effective knowledge infusion module for improving model performance, and a knowledge distillation component for reducing the model size and deploying K-PLMs on resource-restricted devices (e.g., CPU) for real-world application. Importantly, instead of capturing entity knowledge like the majority of existing K-PLMs, our approach captures relational knowledge, which contributes to better improving sentence-level text classification and text matching tasks that play a key role in question answering (QA). We conducted a set of experiments on five text classification tasks and three text matching tasks from three domains, namely E-commerce, Government, and Film&TV, and performed online A/B tests in E-commerce. Experimental results show that our approach is able to achieve substantial improvement on sentence-level question answering tasks and bring beneficial business value in industrial settings.
PeriodicMove: Shift-aware Human Mobility Recovery with Graph Neural Network
Authors: Hao Sun, Changjie Yang, Liwei Deng, Fan Zhou, Feiteng Huang, Kai Zheng
Venue: CIKM
Year: 2021

Human mobility recovery is of great importance for a wide range of location-based services. However, recovering human mobility is not trivial because of three challenges: 1) complex transition patterns among locations; 2) multi-level periodicity and shifting periodicity of human mobility; 3) sparsity of the collected trajectory data. In this paper, we propose PeriodicMove, a neural attention model based on graph neural network for human mobility recovery from lengthy and sparse trajectories. In PeriodicMove, we first construct a directed graph for each trajectory and capture complex location transition patterns using graph neural network. Then, we design two attention mechanisms which capture multi-level periodicity and shifting periodicity of human mobility respectively. Finally, a spatial-aware loss function is proposed to incorporate spatial proximity into the model optimization, which alleviates the data sparsity problem. We perform extensive experiments and the evaluation results demonstrate that PeriodicMove yields significant improvements over the competitors on two representative real-life mobility datasets. In addition, by providing high-quality mobility data, our model can benefit a variety of mobility-oriented downstream applications.
DESYR: Definition and Syntactic Representation Based Claim Detection on the Web
Authors: Megha Sundriyal, Parantak Singh, Md. Shad Akhtar, Shubhashis Sengupta, Tanmoy Chakraborty
Venue: CIKM
Year: 2021

The formulation of a claim rests at the core of argument mining. To demarcate between a claim and a non-claim is arduous for both humans and machines, owing to latent linguistic variance between the two and the inadequacy of extensive definition-based formalization. Furthermore, the increase in the usage of online social media has resulted in an explosion of unsolicited information on the web presented as informal text. To account for the aforementioned, in this paper, we propose DESYR. It is a framework that intends on annulling the said issues for informal web-based text by leveraging a combination of hierarchical representation learning (dependency-inspired Poincaré embedding), definition-based alignment, and feature projection. We do away with fine-tuning compute-heavy language models in favor of fabricating a more domain-centric but lighter approach. Experimental results indicate that DESYR builds upon the state-of-the-art system across four benchmark claim datasets, most of which were constructed with informal texts. We see an increase of 3 claim-F1 points on the LESA-Twitter dataset, an increase of 1 claim-F1 point and 9 macro-F1 points on the Online Comments (OC) dataset, an increase of 24 claim-F1 points and 17 macro-F1 points on the Web Discourse (WD) dataset, and an increase of 8 claim-F1 points and 5 macro-F1 points on the Micro Texts (MT) dataset. We also perform an extensive analysis of the results. We make a 100-D pre-trained version of our Poincaré-variant along with the source code.
Actionable Insights in Urban Multivariate Time-series
Authors: Anika Tabassum, Supriya Chinthavali, Varisara Tansakul, B. Aditya Prakash
Venue: CIKM
Year: 2021

Multivariate time-series data are gaining popularity in various urban applications, such as emergency management, public health, etc. Segmentation algorithms mostly focus on identifying discrete events with changing phases in such data. For example, consider a power outage scenario during a hurricane. Each time-series can represent the number of power failures in a county for a time period. Segments in such time-series are found in terms of different phases, such as, when a hurricane starts, counties face severe damage, and hurricane ends. Disaster management domain experts typically want to identify the most affected counties (time-series of interests) during these phases. These can be effective for retrospective analysis and decision-making for resource allocation to those regions to lessen the damage. However, getting these actionable counties directly (either by simple visualization or looking into the segmentation algorithm) is typically hard. Hence we introduce and formalize a novel problem RaTSS (Rationalization for time-series segmentation) that aims to find such time-series (rationalizations), which are actionable for the segmentation. We also propose an algorithm Find-RaTSS to find them for any black-box segmentation. We show Find-RaTSS outperforms non-trivial baselines on generalized synthetic and real data, also provides actionable insights in multiple urban domains, especially disasters and public health.
Counterfactual Explainable Recommendation
Authors: Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, Yongfeng Zhang
Venue: CIKM
Year: 2021

By providing explanations for users and system designers to facilitate better understanding and decision making, explainable recommendation has been an important research problem. In this paper, we propose Counterfactual Explainable Recommendation (CountER), which takes the insights of counterfactual reasoning from causal inference for explainable recommendation. CountER is able to formulate the complexity and the strength of explanations, and it adopts a counterfactual learning framework to seek simple (low complexity) and effective (high strength) explanations for the model decision. Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed. These altered aspects constitute the explanation of why the original item is recommended. The counterfactual explanation helps both the users for better understanding and the system designers for better model debugging. Another contribution of the work is the evaluation of explainable recommendation, which has been a challenging task. Fortunately, counterfactual explanations are very suitable for standard quantitative evaluation. To measure the explanation quality, we design two types of evaluation metrics, one from user's perspective (i.e. why the user likes the item), and the other from model's perspective (i.e. why the item is recommended by the model). We apply our counterfactual learning algorithm on a black-box recommender system and evaluate the generated explanations on five real-world datasets. Results show that our model generates more accurate and effective explanations than state-of-the-art explainable recommendation models. Source code is available at https://github.com/chrisjtan/counter.
On Influencing the Influential: Disparity Seeding
Authors: Ya-Wen Teng, Hsi-Wen Chen, De-Nian Yang, Yvonne-Anne Pignolet, Ting-Wei Li, Lydia Yiyu Chen
Venue: CIKM
Year: 2021

Online social networks have become a crucial medium to disseminate the latest political, commercial, and social information. Users with high visibility are often selected as seeds to spread information and affect their adoption in target groups. We study how gender differences and similarities can impact the information spreading process. Using a large-scale Instagram dataset and a small-scale Facebook dataset, we first conduct a multi-faceted analysis taking the interaction type, directionality and frequency into account. To this end, we explore a variety of existing and new single and multihop centrality measures. Our analysis unveils that males and females interact differently depending on the interaction types, e.g., likes or comments, and they feature different support and promotion patterns. We complement prior work showing that females do not reach top visibility (often referred to as the glass ceiling effect) jointly factoring in the connectivity and interaction intensity, both of which were previously mainly discussed independently. Inspired by these observations, we propose a novel seeding framework, called Disparity Seeding, which aims at maximizing spread while reaching a target user group, e.g., a certain percentage of females - promoting the influence of under-represented groups. Disparity Seeding ranks influential users with two gender-aware measures, the Target HI-index and the Embedding index. Extensive simulations comparing Disparity Seeding with target-agnostic algorithms show that Disparity Seeding meets the target percentage while effectively maximizing the spread. Disparity Seeding can be generalized to counter different types of inequality, e.g., race, and proactively promote minorities in the society.
Self-supervised Representation Learning on Dynamic Graphs
Authors: Sheng Tian, Ruofan Wu, Leilei Shi, Liang Zhu, Tao Xiong
Venue: CIKM
Year: 2021

Graph representation learning has now become the de facto standard when dealing with graph-structured data. Using powerful tools from deep learning and graph neural networks, recent works have applied graph representation learning to time-evolving dynamic graphs and showed promising results. However, all the previous dynamic graph models require labeled samples to train, which might be costly to acquire in practice. Self-supervision offers a principled way of utilizing unlabeled data and has achieved great success in computer vision community. In this paper we propose debiased dynamic graph contrastive learning (DDGCL), the first self-supervised representation learning framework on dynamic graphs. The proposed model extends the contrastive learning idea to dynamic graphs via contrasting two nearby temporal views of the same node identity, with a time-dependent similarity critic. Inspired by recent theoretical developments contrastive learning, we propose a novel debiased GAN-type contrastive loss as the learning objective in order to correct the sampling bias occurred in negative sample construction process. We conduct extensive experiments on benchmark datasets via testing the DDGCL framework under two different self-supervision schemes: pretraining and finetuning and multi task learning. The results show that using a simple time-aware GNN encoder, the performance of downstream tasks is significantly improved under either scheme to closely match, or even outperform state-of-the-art dynamic graph models with more elegant encoder architectures. Further empirical evaluations suggest that the proposed approach offers more performance improvement than previously established self-supervision mechanisms over static graphs.
Recipe Representation Learning with Networks
Authors: Yijun Tian, Chuxu Zhang, Ronald A. Metoyer, Nitesh V. Chawla
Venue: CIKM
Year: 2021

Learning effective representations for recipes is essential in food studies for recommendation, classification, and other applications. Unlike what has been developed for learning textual or cross-modal embeddings for recipes, the structural relationship among recipes and food items are less explored. In this paper, we formalize the problem recipe representation learning with networks to involve both the textual feature and the structural relational feature into recipe representations. Specifically, we first present RecipeNet, a new and large-scale corpus of recipe data to facilitate network based food studies and recipe representation learning research. We then propose a novel heterogeneous recipe network embedding model, rn2vec, to learn recipe representations. The proposed model is able to capture textual, structural, and nutritional information through several neural network modules, including textual CNN, inner-ingredients transformer, and a graph neural network with hierarchical attention. We further design a combined objective function of node classification and link prediction to jointly optimize the model. The extensive experiments show that our model outperforms state-of-the-art baselines on two classic food study tasks. Dataset and codes are available at https://github.com/meettyj/rn2vec.
AudiBERT: A Deep Transfer Learning Multimodal Classification Framework for Depression Screening
Authors: Ermal Toto, M. L. Tlachac, Elke A. Rundensteiner
Venue: CIKM
Year: 2021

Depression is a leading cause of disability with tremendous socioeconomic costs. In spite of early detection being crucial to improving prognosis, this mental illness remains largely undiagnosed. Depression classification from voice holds the promise to revolutionize diagnosis by ubiquitously integrating this screening capability into virtual assistants and smartphone technologies. Unfortunately, due to privacy concerns, audio datasets with depression labels have a small number of participants, causing current classification models to suffer from low performance. To tackle this challenge, we introduce Audio-Assisted BERT (AudiBERT), a novel deep learning framework that leverages the multimodal nature of human voice. To alleviate the small data problem, AudiBERT integrates pretrained audio and text representation models for the respective modalities augmented by a dual self-attention mechanism into a deep learning architecture. AudiBERT applied to depression classification consistently achieves promising performance with an increase in F1 scores between 6% and 30% compared to state-of-the-art audio and text models for 15 thematic question datasets. Using answers from medically targeted and general wellness questions, our framework achieves F1 scores of up to 0.92 and 0.86, respectively, demonstrating the feasibility of depression screening from informal dialogue using voice-enabled technologies.
Toward an Effective Black-Box Adversarial Attack on Functional JavaScript Malware against Commercial Anti-Virus
Authors: Yun-Da Tsai, Cheng-Kuan Chen, Shou-De Lin
Venue: CIKM
Year: 2021

Machine learning has been a rising technique in signatureless malware detection and is popular in the anti-virus industry. Despite the powerful ability of machine learning, it is known to be vulnerable to attack by injecting specially crafted input noise (adversarial example). In this paper, we develop a systematic attack method that is effective, general and also efficient which automatically generates functional malware. Experiment results showed that such adversarial malware could deceive commercial anti-virus and completely defeat learning-based malware detector provided by a well-known anti-virus vendor. We further examine the effectiveness of our approach on multiple anti-virus engines on VirusTotal and investigate the transferability of our proposed method between different features and classification algorithms. Finally, we show how our attack could resist JavaScript de-obfuscation techniques.
Attention Based Dynamic Graph Learning Framework for Asset Pricing
Authors: Ajim Uddin, Xinyuan Tao, Dantong Yu
Venue: CIKM
Year: 2021

Recent studies suggest that financial networks play an essential role in asset valuation and investment decisions. Unlike road networks, financial networks are neither given nor static, posing significant challenges in learning meaningful networks and promoting their applications in price prediction. In this paper, we first apply the attention mechanism to connect the "dots" (firms) and learn dynamic network structures among stocks over time. Next, the end-to-end graph neural networks pipeline diffuses and propagates the firms' accounting fundamentals into the learned networks and ultimately predicts stock future returns. The proposed model reduces the prediction errors by 6% compared to the state-of-the-art models. Our results are robust with different assessment measures. We also show that portfolios based on our model outperform the S&P-500 index by 34% in terms of Sharpe Ratio, suggesting that our model is better at capturing the dynamic inter-connection among firms and identifying stocks with fast recovery from major events. Further investigation on the learned networks reveals that the network structure aligns closely with the market conditions. Finally, with an ablation study, we investigate different alternative versions of our model and the contribution of each component.
Human-Centric Analytics and Systems Impacting Quality of Life: ECG Analytics and Beyond
Authors: Arijit Ukil, Leandro Marín, Antonio J. Jara, John R. Farserotu
Venue: CIKM
Year: 2021

With the proliferation of Internet of Things (IoT), sensor technologies and advancement in the analytics systems mainly in the form of deep learning algorithms, the long quest for developing human-centric applications like automated disease diagnosis, privacy-enabled analytics is becoming a reality. Knowledge-Driven Analytics and Systems Impacting Human Quality of Life (KDAH) is indeed an attractive proposition. For example, Phonocardiogram or heart sound-based detection of heart abnormality or Myocardial Infarction (MI) prediction using single lead Electrocardiogram (ECG) signals are signifying the capability of analytics algorithm to directly solve human-centric problems. We have proposed privacy-preserved analytics with phonocardiogram-based heart condition detection, established benchmark performance of MI detection using single lead ECG signals as well as demonstrated deep residual learning based algorithm to help carbon footprint management.
Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank
Authors: Ali Vardasbi, Maarten de Rijke, Ilya Markov
Venue: CIKM
Year: 2021

In counterfactual learning to rank (CLTR) user interactions are used as a source of supervision. Since user interactions come with bias, an important focus of research in this field lies in developing methods to correct for the bias of interactions. Inverse propensity scoring (IPS) is a popular method suitable for correcting position bias. Affine correction (AC) is a generalization of IPS that corrects for position bias and trust bias. IPS and AC provably remove bias, conditioned on an accurate estimation of the bias parameters. Estimating the bias parameters, in turn, requires an accurate estimation of the relevance probabilities. This cyclic dependency introduces practical limitations in terms of sensitivity, convergence and efficiency. We propose a new correction method for position and trust bias in CLTR in which, unlike the existing methods, the correction does not rely on relevance estimation. Our proposed method, mixture-based correction (MBC), is based on the assumption that the distribution of the CTRs over the items being ranked is a mixture of two distributions: the distribution of CTRs for relevant items and the distribution of CTRs for non-relevant items. We prove that our method is unbiased. The validity of our proof is not conditioned on accurate bias parameter estimation. Our experiments show that MBC, when used in different bias settings and accompanied by different LTR algorithms, outperforms AC, the state-of-the-art method for correcting position and trust bias, in some settings, while performing on par in other settings. Furthermore, MBC is orders of magnitude more efficient than AC in terms of the training time.
SAUCE: Truncated Sparse Document Signature Bit-Vectors for Fast Web-Scale Corpus Expansion
Authors: Muntasir Wahed, Daniel Gruhl, Alfredo Alba, Anna Lisa Gentile, Petar Ristoski, Chad DeLuca, Steve Welch, Ismini Lourentzou
Venue: CIKM
Year: 2021

Recent advances in text representation have shown that training on large amounts of text is crucial for natural language understanding. However, models trained without predefined notions of topical interest typically require careful fine-tuning when transferred to specialized domains. When a sufficient amount of within-domain text may not be available, expanding a seed corpus of relevant documents from large-scale web data poses several challenges. First, corpus expansion requires scoring and ranking each document in the collection, an operation that can quickly become computationally expensive as the web corpora size grows. Relying on dense vector spaces and pairwise similarity adds to the computational expense. Secondly, as the domain concept becomes more nuanced, capturing the long tail of domain-specific rare terms becomes non-trivial, especially under limited seed corpora scenarios. In this paper, we consider the problem of fast approximate corpus expansion given a small seed corpus with a few relevant documents as a query, with the goal of capturing the long tail of a domain-specific set of concept terms. To efficiently collect large-scale domain-specific corpora with limited relevance feedback, we propose a novel truncated sparse document bit-vector representation, termed Signature Assisted Unsupervised Corpus Expansion (SAUCE). Experimental results show that SAUCE can reduce the computational burden while ensuring high within-domain lexical coverage.