Detecting Communities from Heterogeneous Graphs

计算机科学 利用 嵌入 理论计算机科学 社会联系 图形 图嵌入 数据挖掘 人工智能 心理学 计算机安全 心理治疗师
作者
Linhao Luo,Yixiang Fang,Xin Cao,Xiaofeng Zhang,Wenjie Zhang
标识
DOI:10.1145/3459637.3482250
摘要

Community detection, aiming to group the graph nodes into clusters with dense inner-connection, is a fundamental graph mining task. Recently, it has been studied on the heterogeneous graph, which contains multiple types of nodes and edges, posing great challenges for modeling the high-order relationship between nodes. With the surge of graph embedding mechanism, it has also been adopted to community detection. A remarkable group of works use the meta-path to capture the high-order relationship between nodes and embed them into nodes' embedding to facilitate community detection. However, defining meaningful meta-paths requires much domain knowledge, which largely limits their applications, especially on schema-rich heterogeneous graphs like knowledge graphs. To alleviate this issue, in this paper, we propose to exploit the context path to capture the high-order relationship between nodes, and build a Context Path-based Graph Neural Network (CP-GNN) model. It recursively embeds the high-order relationship between nodes into the node embedding with attention mechanisms to discriminate the importance of different relationships. By maximizing the expectation of the co-occurrence of nodes connected by context paths, the model can learn the nodes' embeddings that both well preserve the high-order relationship between nodes and are helpful for community detection. Extensive experimental results on four real-world datasets show that CP-GNN outperforms the state-of-the-art community detection methods1.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
6666发布了新的文献求助10
2秒前
无限雨南发布了新的文献求助10
2秒前
EgoElysia完成签到,获得积分10
2秒前
敏感雅香发布了新的文献求助10
3秒前
归尘发布了新的文献求助150
4秒前
zumri发布了新的文献求助10
4秒前
jia完成签到,获得积分10
6秒前
7秒前
7秒前
hino发布了新的文献求助10
7秒前
共享精神应助6666采纳,获得10
9秒前
shower_009完成签到,获得积分10
10秒前
12秒前
在水一方应助哈哈采纳,获得10
13秒前
13秒前
纯真追命完成签到 ,获得积分10
13秒前
13秒前
14秒前
咚咚锵完成签到,获得积分10
14秒前
14秒前
包容的琦发布了新的文献求助30
17秒前
梦里繁花发布了新的文献求助10
17秒前
Wang完成签到,获得积分10
19秒前
weilanhaian完成签到,获得积分10
19秒前
20秒前
蒋雪琴完成签到 ,获得积分10
20秒前
wjw发布了新的文献求助10
21秒前
22秒前
FashionBoy应助聪慧的正豪采纳,获得10
23秒前
23秒前
李长印发布了新的文献求助10
24秒前
24秒前
weilanhaian发布了新的文献求助10
25秒前
26秒前
nannan发布了新的文献求助10
26秒前
健忘小霜发布了新的文献求助10
26秒前
张大英完成签到 ,获得积分20
28秒前
28秒前
29秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988868
求助须知:如何正确求助?哪些是违规求助? 3531255
关于积分的说明 11253071
捐赠科研通 3269858
什么是DOI,文献DOI怎么找? 1804822
邀请新用户注册赠送积分活动 881994
科研通“疑难数据库(出版商)”最低求助积分说明 809035