{"title":"Learning to Augment Graphs: Machine-Learning-Based Social Network Intervention With Self-Supervision","authors":"Chih-Chieh Chang;Chia-Hsun Lu;Ming-Yi Chang;Chao-En Shen;Ya-Chi Ho;Chih-Ya Shen","doi":"10.1109/TCSS.2023.3340230","DOIUrl":null,"url":null,"abstract":"This article proposes a machine learning (ML)-based approach to solve a graph optimization problem, named network intervention with limited degradation (NILD), which aims at adding new edges to augment the graph to minimize the local clustering coefficient (LCC) of a target node. The main application of NILD is to perform \n<italic>network intervention</i>\n, to improve the mental well-being of individuals. This article proposes a new framework, named network intervention with self-supervision (NISS), which employs reinforcement learning and self-supervised learning (SSL) to effectively solve the problem. We propose two new effective pretext tasks in SSL, \n<italic>Distance-to-target</i>\n prediction task and \n<italic>LCC increment</i>\n prediction task to improve the model performance. In addition, we also propose two new embedding approaches, neighborhood embedding (NE) and constraint property embedding (CPE), to capture the structural information of the graph. Extensive experiments on multiple real social networks and synthetic datasets show that our proposed approach significantly outperforms the other state-of-the-art baselines, including ML-based baselines and deterministic algorithms.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10410424/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
Abstract
This article proposes a machine learning (ML)-based approach to solve a graph optimization problem, named network intervention with limited degradation (NILD), which aims at adding new edges to augment the graph to minimize the local clustering coefficient (LCC) of a target node. The main application of NILD is to perform
network intervention
, to improve the mental well-being of individuals. This article proposes a new framework, named network intervention with self-supervision (NISS), which employs reinforcement learning and self-supervised learning (SSL) to effectively solve the problem. We propose two new effective pretext tasks in SSL,
Distance-to-target
prediction task and
LCC increment
prediction task to improve the model performance. In addition, we also propose two new embedding approaches, neighborhood embedding (NE) and constraint property embedding (CPE), to capture the structural information of the graph. Extensive experiments on multiple real social networks and synthetic datasets show that our proposed approach significantly outperforms the other state-of-the-art baselines, including ML-based baselines and deterministic algorithms.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.