{"title":"学习增强图谱:基于机器学习的社交网络干预与自我监督","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":"{\"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}","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
摘要
本文提出了一种基于机器学习(ML)的方法来解决图优化问题,并将其命名为 "有限退化的网络干预(NILD)",其目的是添加新的边以增强图,从而使目标节点的局部聚类系数(LCC)最小化。NILD 的主要应用是进行网络干预,以改善个人的心理健康。本文提出了一个新的框架,名为 "自我监督网络干预(NISS)",它采用强化学习和自我监督学习(SSL)来有效解决这一问题。我们在 SSL 中提出了两个新的有效借口任务,即目标距离预测任务和 LCC 增量预测任务,以提高模型性能。此外,我们还提出了两种新的嵌入方法:邻域嵌入(NE)和约束属性嵌入(CPE),以捕捉图的结构信息。在多个真实社交网络和合成数据集上的广泛实验表明,我们提出的方法明显优于其他最先进的基线方法,包括基于 ML 的基线方法和确定性算法。
Learning to Augment Graphs: Machine-Learning-Based Social Network Intervention With Self-Supervision
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.