OpenRaiser/Intern-Atlas huggingface dataset:
Faster Approximation Algorithms for Parameterized Graph Clustering and Edge Labeling
Authors: Vedangi Bengali, Nate Veldt
Venue: CIKM
Year: 2023

Graph clustering is a fundamental task in network analysis where the goal is to detect sets of nodes that are well-connected to each other but sparsely connected to the rest of the graph. We present faster approximation algorithms for an NP-hard parameterized clustering framework called LambdaCC, which is governed by a tunable resolution parameter and generalizes many other clustering objectives such as modularity, sparsest cut, and cluster deletion. Previous LambdaCC algorithms are either heuristics with no approximation guarantees, or computationally expensive approximation algorithms. We provide fast new approximation algorithms that can be made purely combinatorial. These rely on a new parameterized edge labeling problem we introduce that generalizes previous edge labeling problems that are based on the principle of strong triadic closure and are of independent interest in social network analysis. Our methods are orders of magnitude more scalable than previous approximation algorithms and our lower bounds allow us to obtain a posteriori approximation guarantees for previous heuristics that have no approximation guarantees of their own.
Relevance-based Infilling for Natural Language Counterfactuals
Authors: Lorenzo Betti, Carlo Abrate, Francesco Bonchi, Andreas Kaltenbrunner
Venue: CIKM
Year: 2023

Counterfactual explanations are a natural way for humans to gain understanding and trust in the outcomes of complex machine learning algorithms. In the context of natural language processing, generating counterfactuals is particularly challenging as it requires the generated text to be fluent, grammatically correct, and meaningful. In this study, we improve the current state of the art for the generation of such counterfactual explanations for text classifiers. Our approach, named RELITC (Relevance-based Infilling for Textual Counterfactuals), builds on the idea of masking a fraction of text tokens based on their importance in a given prediction task and employs a novel strategy, based on the entropy of their associated probability distributions, to determine the infilling order of these tokens. Our method uses less time than competing methods to generate counterfactuals that require less changes, are closer to the original text and preserve its content better, while being competitive in terms of fluency. We demonstrate the effectiveness of the method on four different datasets and show the quality of its outcomes in a comparison with human generated counterfactuals.
Data and Decision Fusion with Uncertainty Quantification for ML-based Healthcare Decision Systems
Authors: Grigor Bezirganyan
Venue: CIKM
Year: 2023

This paper outlines the PhD research plan to develop a comprehensive, uncertainty-aware, multimodal deep learning approach to be used in the healthcare domain. The goal is to design a multimodal deep learning framework that can leverage the complex interconnections between various modalities in order to generate highly precise predictions for intricate healthcare datasets. In addition, the framework should also incorporate methods for quantifying and dealing with uncertainty, which is an important consideration in many real-world healthcare applications. The approach will be tested on real-world multimodal datasets from Marseilles hospitals in France. We further represent some preliminary results of our early stage experiments with uncertainty quantification on multimodal datasets.
RT2S: A Framework for Learning with Noisy Labels
Authors: Indranil Bhattacharya, Ze Ye, Kaushik Pavani, Sunny Dasgupta
Venue: CIKM
Year: 2023

We introduce Robust Training with Trust Scores (RT2S), a framework to train machine learning classifiers with potentially noisy labels. RT2S calculates a trust score for each training sample, which indicates the quality of its corresponding label. These trust scores are employed as sample weights during training and optionally during threshold optimization. The trust scores are generated from two sources: (i) the model's confidence in the observed label, leveraging out-of-fold prediction scores to detect anomalous labels in the training data, and (ii) the probability of the correct label, ascertained by a Large Language Model with the ability to identify biased label noise. We evaluate RT2S by training machine learning models on 6 product classification datasets that utilize low-quality labels generated by a rule-based classification engine acting as a surrogate labeler. Our experimental findings indicate that RT2S outperforms all baselines, and achieves an average accuracy improvement of 4.38% (max 7.18%) over rule-based classifiers in particular.
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning
Authors: Qingtian Bian, Jiaxing Xu, Hui Fang, Yiping Ke
Venue: CIKM
Year: 2023

The motivations of users to make interactions can be divided into static preference and dynamic interest. To accurately model user representations over time, recent studies in sequential recommendation utilize information propagation and evolution to mine from batches of arriving interactions. However, they ignore the fact that people are easily influenced by the recent actions of other users in the contextual scenario, and applying evolution across all historical interactions dilutes the importance of recent ones, thus failing to model the evolution of dynamic interest accurately. To address this issue, we propose a Context-Aware Pseudo-Multi-Task Recommender System (CPMR) to model the evolution in both historical and contextual scenarios by creating three representations for each user and item under different dynamics: static embedding, historical temporal states, and contextual temporal states. To dually improve the performance of temporal states evolution and incremental recommendation, we design a Pseudo-Multi-Task Learning (PMTL) paradigm by stacking the incremental single-target recommendations into one multi-target task for joint optimization. Within the PMTL paradigm, CPMR employs a shared-bottom network to conduct the evolution of temporal states across historical and contextual scenarios, as well as the fusion of them at the user-item level. In addition, CPMR incorporates one real tower for incremental predictions, and two pseudo towers dedicated to updating the respective temporal states based on new batches of interactions. Experimental results on four benchmark recommendation datasets show that CPMR consistently outperforms state-of-the-art baselines and achieves significant gains on three of them. The source code is available at https://github.com/DiMarzioBian/CPMR.
Enabling Health Data Sharing with Fine-Grained Privacy
Authors: Luca Bonomi, Sepand Gousheh, Liyue Fan
Venue: CIKM
Year: 2023

Sharing health data is vital in advancing medical research and transforming knowledge into clinical practice. Meanwhile, protecting the privacy of data contributors is of paramount importance. To that end, several privacy approaches have been proposed to protect individual data contributors in data sharing, including data anonymization and data synthesis techniques. These approaches have shown promising results in providing privacy protection at the dataset level. In this work, we study the privacy challenges in enabling fine-grained privacy in health data sharing. Our work is motivated by recent research findings, in which patients and healthcare providers may have different privacy preferences and policies that need to be addressed. Specifically, we propose a novel and effective privacy solution that enables data curators (e.g., healthcare providers) to protect sensitive data elements while preserving data usefulness. Our solution builds on randomized techniques to provide rigorous privacy protection for sensitive elements and leverages graphical models to mitigate privacy leakage due to dependent elements. To enhance the usefulness of the shared data, our randomized mechanism incorporates domain knowledge to preserve semantic similarity and adopts a block-structured design to minimize utility loss. Evaluations with real-world health data demonstrate the effectiveness of our approach and the usefulness of the shared data for health applications.
From User Activity Traces to Navigation Graph for Software Enhancement: An Application of Graph Neural Network (GNN) on a Real-World Non-Attributed Graph
Authors: Ikram Boukharouba, Florence Sèdes, Christophe Bortolaso, Florent Mouysset
Venue: CIKM
Year: 2023

Understanding software's user behavior is key to personalizing and enriching the user experience and improving the quality of the software. In this paper, we consider the use of user navigation graphs issued from user activity traces. The aim of our study is to do node classification over the user graph navigation in order to understand better the composition of the software and to offer a better experience to the users. Traditional baseline methods has shown good performance in the node classification task, but can't be applied for tasks as link prediction. Graph Neural Network on the contrary can satisfy both node classification and link prediction. However, GNN produce significant results when the features on the nodes are numerous enough. This is not always the case in real-world problems, because too many features implies too much data, storage issues, affect the performances of apps, etc. Indeed, due to the origin of the data and their uncontrolled generation, the resulting graphs contain few or no features (AKA non-attributed graphs). In addition, in industrial fields, some external requirements particularly legal may limit the collection and the use of data. In this article, we show that graphs issued from real-world data also have such limitations, and we propose the generation of artificial features on the nodes as a solution to this problem. The obtained results showed that the usage of artificial node features is a promising solution to overcome the greediness of GNN in terms of node features and applying GNN on the non-attributed graphs.
Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility
Authors: Zeyd Boukhers, Azeddine Bouabdallah, Cong Yang, Jan Jürjens
Venue: CIKM
Year: 2023

Since Bitcoin first appeared on the scene in 2009, cryptocurrencies have become a worldwide phenomenon as important decentralized financial assets. Their decentralized nature, however, leads to notable volatility against traditional fiat currencies, making the task of accurately forecasting the crypto-fiat exchange rate complex. In this study, we examine the various independent factors that affect the Bitcoin-Dollar exchange rate's volatility. To this end, we propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making. We have subjected this method to extensive testing via comprehensive experiments, thereby validating the importance of multimodal combination over exclusive reliance on trading data. Further experiments show that our method significantly surpasses existing forecasting tools and methodologies, demonstrating a 19.29% improvement. This result underscores the influence of external independent factors on cryptocurrency volatility.
Fair&Share: Fast and Fair Multi-Criteria Selections
Authors: Kathleen Cachel, Elke A. Rundensteiner
Venue: CIKM
Year: 2023

Traditional multi-criteria selection methods are the leading approach for selecting a set of candidates when multiple criteria determine selection relevancy. For instance, hiring platforms combine candidates' proximity, skills, and years of experience to build shortlists for recruiters. While these methods succeed in efficiently selecting candidates, their chosen set may unfairly affect marginalized candidate groups (e.g., race or gender). Bridging the gap between traditional fairness-unaware multi-criteria selection and contemporary fairness interventions, we characterize the open problem of fair multi-criteria selection. We design Fair&Share the first efficient fairness-tunable multi-criteria selection method. Fair&Share supports several fair representation notions. The key to Fair&Share is the design of its group-aware utility objective. Fair&Share uses a novel fairness calibration component to provide a user-friendly tuning mechanism for controlling the balance between selection relevancy (utility) and representation fairness. Our fairness-focused selection policy iteratively builds the result set by prioritizing candidates as aiding either the fair representation or the shared overall utility goals. We prove the optimality of Fair&Share, meaning that Fair&Share selects the best possible candidates such that the desired fair representation is achieved. Our experimental study demonstrates that Fair&Share achieves the best fairness and utility performance of state-of-the-art alternatives adapted to this new problem while taking a fraction of the time.
CallMine: Fraud Detection and Visualization of Million-Scale Call Graphs
Authors: Mirela Teixeira Cazzolato, Saranya Vijayakumar, Meng-Chieh Lee, Catalina Vajiac, Namyong Park, Pedro Fidalgo, Agma J. M. Traina, Christos Faloutsos
Venue: CIKM
Year: 2023

Given a million-scale dataset of who-calls-whom data containing imperfect labels, how can we detect existing and new fraud patterns? We propose CallMine, with carefully designed features and visualizations. Our CallMine method has the following properties: (a) Scalable, being linear on the input size, handling about 35 million records in around one hour on a stock laptop; (b) Effective, allowing natural interaction with human analysts; (c) Flexible, being applicable in both supervised and unsupervised settings; (d) Automatic, requiring no user-defined parameters. In the real world, in a multi-million-scale dataset, CallMine was able to detect fraudsters 7,000x faster, namely in a matter of hours, while expert humans took over 10 months to detect them. CIKM-ARP Categories: Application; Analytics and machine learning; Data presentation.
Region-Wise Attentive Multi-View Representation Learning For Urban Region Embedding
Authors: Weiliang Chan, Qianqian Ren
Venue: CIKM
Year: 2023

Urban region embedding is an important and yet highly challenging issue due to the complexity and constantly changing nature of urban data. To address the challenges, we propose a Region-Wise Multi-View Representation Learning (ROMER) to capture multi-view dependencies and learn expressive representations of urban regions without the constraints of rigid neighbourhood region conditions. Our model focuses on learn urban region representation from multi-source urban data. First, we capture the multi-view correlations from mobility flow patterns, POI semantics and check-in dynamics. Then, we adopt global graph attention networks to learn similarity of any two vertices in graphs. To comprehensively consider and share features of multiple views, a two-stage fusion module is further proposed to learn weights with external attention to fuse multi-view embeddings. Extensive experiments for two downstream tasks on real-world datasets demonstrate that our model outperforms state-of-the-art methods by up to 17% improvement.
'Choose your Data Wisely': Active Learning based Selection with Multi-Objective Optimisation for Mitigating Stereotypes
Authors: Manish Chandra, Debasis Ganguly, Tulika Saha, Iadh Ounis
Venue: CIKM
Year: 2023

Data-driven (deep) learning methods has led to parameterised abstractions of the data, often leading to stereotype societal biases in their predictions, e.g., predicting more frequently that women are weaker than men, or that African Americans are more likely to commit crimes than Caucasians. Standard approaches of mitigating such stereotypical biases from deep neural models include modifying the training dataset (pre-processing), or adjusting the model parameters with a bias-specific objective (in-processing). In our work, we approach this bias mitigation from a different perspective - that of an active learning-based selection of a subset of data instances towards training a model optimised for both effectiveness and fairness. Specifically speaking, the imbalances in the attribute value priors can be alleviated by constructing a balanced subset of the data instances with two selection objectives - first, of improving the model confidence of the primary task itself (a standard practice in active learning), and the second, of taking into account the parity of the model predictions with respect to the sensitive attributes, such as gender and race etc. We demonstrate that our proposed selection function achieves better results in terms of both the primary task effectiveness and fairness. The results are further shown to improve when this active learning-based data selection is combined with an in-process method of multi-objective training.
MUWS'2023: The 2nd International Workshop on Multimodal Human Understanding for the Web and Social Media
Authors: Gullal S. Cheema, Sherzod Hakimov, Marc A. Kastner, Noa Garcia
Venue: CIKM
Year: 2023

Multimodal human understanding and analysis is an emerging research area that cuts through several disciplines like Computer Vision, Natural Language Processing (NLP), Speech Processing, Human-Computer Interaction, and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences, semiotics and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights and analysis on perceptual understanding through signs and symbols via multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media. The theme of the MUWS workshop, multimodal human understanding, includes various interdisciplinary challenges related to social bias analyses, multimodal representation learning, detection of human impressions or sentiment, hate speech, sarcasm in multimodal data, multimodal rhetoric and semantics, and related topics. The MUWS workshop will be an interactive event and include keynotes by relevant experts, poster and demo sessions, research presentations and discussion.
Inducing Causal Structure for Abstractive Text Summarization
Authors: Lu Chen, Ruqing Zhang, Wei Huang, Wei Chen, Jiafeng Guo, Xueqi Cheng
Venue: CIKM
Year: 2023

The mainstream of data-driven abstractive summarization models tends to explore the correlations rather than the causal relationships. Among such correlations, there can be spurious ones which suffer from the language prior learned from the training corpus and therefore undermine the overall effectiveness of the learned model. To tackle this issue, we introduce a Structural Causal Model (SCM) to induce the underlying causal structure of the summarization data. We assume several latent causal factors and non-causal factors, representing the content and style of the document and summary. Theoretically, we prove that the latent factors in our SCM can be identified by fitting the observed training data under certain conditions. On the basis of this, we propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq) to learn the causal representations that can mimic the causal factors, guiding us to pursue causal information for summary generation. The key idea is to reformulate the Variational Auto-encoder (VAE) to fit the joint distribution of the document and summary variables from the training corpus. Experimental results on two widely used text summarization datasets demonstrate the advantages of our approach.
Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data
Authors: Zhichao Chen, Leilei Ding, Zhixuan Chu, Yucheng Qi, Jianmin Huang, Hao Wang
Venue: CIKM
Year: 2023

Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios. However, existing time-series forecasting methods often overlook two important characteristics of cumulative data, namely monotonicity and irregularity, which limit their practical applicability. To address this limitation, we propose a principled approach called Monotonic neural Ordinary Differential Equation (MODE) within the framework of neural ordinary differential equations. By leveraging MODE, we are able to effectively capture and represent the monotonicity and irregularity in practical cumulative data. Through extensive experiments conducted in a bonus allocation scenario, we demonstrate that MODE outperforms state-of-the-art methods, showcasing its ability to handle both monotonicity and irregularity in cumulative data and delivering superior forecasting performance.
Learning Pair-Centric Representation for Link Sign Prediction with Subgraph
Authors: Jushuo Chen, Feifei Dai, Xiaoyan Gu, Haihui Fan, Jiang Zhou, Bo Li, Weiping Wang
Venue: CIKM
Year: 2023

Signed graphs are prevalent data structures containing both positive and negative links. Recently, the fundamental network analysis task on signed graphs, namely link sign prediction, has received careful attention. Existing methods learn two target node representations independently, and the sign between these two nodes is predicted based on similarity. However, such a paradigm is node-centric that cannot distinguish node pairs with distinct contexts, thus lowering the prediction performance. Learning pair-centric representation is therefore a rewarding way to be aware of differences between pairs. There is no study yet on how to build such an appropriate representation that can effectively infer the sign between the target node pair. In this paper, we provide a new perspective to conduct link sign prediction within the paradigm of subgraph classification and propose a novel Subgraph-based link Sign Prediction (SSP) model. Technically, SSP uses importance-based sampling to extract an informative subgraph around each target node pair. For each subgraph, an innovative node labeling scheme is designed to encode its structural and signed information for representation learning. To further utilize the subgraph representation for imbalanced sign classification, SSP employs self-pruning contrastive learning to gain balanced representations. Extensive experiments on real-world datasets demonstrate that SSP consistently and significantly outperforms all the state-of-the-art baselines.