Massive social media data can reflect people's authentic thoughts, emotions, communication, etc., and therefore can be analyzed for early detection of mental health problems such as depression. Existing works about early depression detection on social media lacked interpretability and neglected the heterogeneity of social media data. Furthermore, they overlooked the global interaction among users. To address these issues, we develop a novel method that leverages a Heterogeneous Subgraph Network with Prompt Learning(HSNPL) and contrastive learning mechanisms. Specifically, prompt learning is employed to map users' implicit psychological symbols with excellent interpretability while deep semantic and diverse behavioral features are incorporated by a heterogeneous information network. Then, the heterogeneous graph network with a dual attention mechanism is constructed to model the relationships among heterogeneous social information at the feature level. Furthermore, the heterogeneous subgraph network integrating subgraph attention and self-supervised contrastive learning is developed to explore complicated interactions among users and groups at the user level. Extensive experimental results demonstrate that our proposed method significantly outperforms state-of-the-art methods for depression detection on social media.
Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology
Ocean University of China（中国海洋大学）；The Affiliated Hospital of Qingdao University（青岛大学附属医院）；Chengdu University of Traditional Chinese Medicine（成都中医药大学）；Southwest Jiaotong University（西南交通大学）
Unveiling and Steering Connectome Organization with Interpretable Latent Variables
National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（国家多媒体信息处理重点实验室，计算机科学学院，北京大学）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（计算机科学与工程学院，电子科学与技术大学）
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
School of Software Technology, Zhejiang University（浙江大学软件学院）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）；Big Graph Center, School of Computer and Computing Science, Hangzhou City University（杭州城市大学计算机与计算科学学院大图中心）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
We generalize triadic closure, along with previous generalizations of triadic closure, under an intuitive umbrella generalization: the Subgraph-to-Subgraph Transition (SST). We present algorithms and code to model graph evolution in terms of collections of these SSTs. We then use the SST framework to create link prediction models for both static and temporal, directed and undirected graphs which produce highly interpretable results. Quantitatively, our models match out-of-the-box performance of state of the art graph neural network models, thereby validating the correctness and meaningfulness …
ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Social Pain Disrupts Emotion-Action Brain-State Dynamics in Adolescents with Non-Suicidal Self-Injury
School of Biomedical Engineering, Medical School, Shenzhen University（深圳大学医学院生物医学工程学院）；Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging（广东省生物医学测量与超声成像重点实验室）；Pengcheng Laboratory（鹏城实验室）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Maternity and Child Healthcare Hospital（深圳市妇幼保健院）
Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations
Data Science Institute, School of Mathematics, Shandong University（数据科学研究院、数学学院、山东大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（数学与系统科学研究院、中国科学院）；Data Science Institute, Shandong University（数据科学研究院、山东大学）
GALAX: Graph-Augmented Language Model for Explainable Reinforcement-Guided Subgraph Reasoning in Precision Medicine
I2DB, Washington University School of Medicine（I2DB，华盛顿大学医学学院）；Department of Computer Science and Engineering, Washington University in St. Louis（计算机科学与工程系，华盛顿大学圣路易斯分校）；Department of Genetics, Washington University School of Medicine（遗传学系，华盛顿大学医学学院）
Feinberg School of Medicine（范伯格医学院）；Northwestern University（西北大学）
Hypergraph neural networks can model multi-way connections among nodes of the graphs, which are common in real-world applications such as genetic medicine. In particular, genetic pathways or gene sets encode molecular functions driven by multiple genes, naturally represented as hyperedges. Thus, hypergraph-guided embedding can capture functional relations in learned representations. Existing hypergraph neural network models often focus on node-level or graph-level inference. There is an unmet need in learning powerful representations of subgraphs of hypergraphs in real-world applications. For …
Yale University（耶鲁大学）；IBM T. J. Watson Research Center（IBM T. J. 沃森研究中心）
Graph kernels are historically the most widely-used technique for graph classification tasks. However, these methods suffer from limited performance because of the hand-crafted combinatorial features of graphs. In recent years, graph neural networks (GNNs) have become the state-of-the-art method in downstream graph-related tasks due to their superior performance. Most GNNs are based on Message Passing Neural Network (MPNN) frameworks. However, recent studies show that MPNNs can not exceed the power of the Weisfeiler-Lehman (WL) algorithm in graph isomorphism test. To address the limitations of…
Rumor Detection with Diverse Counterfactual Evidence
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Mining human-brain networks to discover patterns that can be used to discriminate between healthy individuals and patients affected by some neurological disorder, is a fundamental task in neuroscience. Learning simple and interpretable models is as important as mere classification accuracy. In this paper we introduce a novel approach for classifying brain networks based on extracting contrast subgraphs, i.e., a set of vertices whose induced subgraphs are dense in one class of graphs and sparse in the other. We formally define the problem and present an algorithmic solution for extracting contr…
GISExplainer: On Explainability of Graph Neural Networks via Game-theoretic Interaction Subgraphs
School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications（重庆邮电大学网络空间安全与信息法学院）；School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）；Chengdu University of Technology（成都理工大学）；School of Software Engineering, Chengdu University of Information Technology（成都信息工程大学软件工程学院）；School of Computer and Information Science, Chongqing Normal University（重庆师范大学计算机与信息科学学院）
