Deep Overlapping Community Search via Subspace Embedding

Fuente: arXiv

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Bibliographic Details
Main Authors:	Sima, Qing, Yu, Jianke, Wang, Xiaoyang, Zhang, Wenjie, Zhang, Ying, Lin, Xuemin
Format:	Preprint
Published:	2024
Subjects:	Social and Information Networks Databases Physics and Society
Online Access:	Acceder al recurso
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author	Sima, Qing Yu, Jianke Wang, Xiaoyang Zhang, Wenjie Zhang, Ying Lin, Xuemin
author_facet	Sima, Qing Yu, Jianke Wang, Xiaoyang Zhang, Wenjie Zhang, Ying Lin, Xuemin
contents	Overlapping Community Search (OCS) identifies nodes that interact with multiple communities based on a specified query. Existing community search approaches fall into two categories: algorithm-based models and Machine Learning-based (ML) models. Despite the long-standing focus on this topic within the database domain, current solutions face two major limitations: 1) Both approaches fail to address personalized user requirements in OCS, consistently returning the same set of nodes for a given query regardless of user differences. 2) Existing ML-based CS models suffer from severe training efficiency issues. In this paper, we formally redefine the problem of OCS. By analyzing the gaps in both types of approaches, we then propose a general solution for OCS named Sparse Subspace Filter (SSF), which can extend any ML-based CS model to enable personalized search in overlapping structures. To overcome the efficiency issue in the current models, we introduce Simplified Multi-hop Attention Networks (SMN), a lightweight yet effective community search model with larger receptive fields. To the best of our knowledge, this is the first ML-based study of overlapping community search. Extensive experiments validate the superior performance of SMN within the SSF pipeline, achieving a 13.73% improvement in F1-Score and up to 3 orders of magnitude acceleration in model efficiency compared to state-of-the-art approaches.
format	Preprint
id	arxiv_https___arxiv_org_abs_2404_14692
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Deep Overlapping Community Search via Subspace Embedding Sima, Qing Yu, Jianke Wang, Xiaoyang Zhang, Wenjie Zhang, Ying Lin, Xuemin Social and Information Networks Databases Physics and Society Overlapping Community Search (OCS) identifies nodes that interact with multiple communities based on a specified query. Existing community search approaches fall into two categories: algorithm-based models and Machine Learning-based (ML) models. Despite the long-standing focus on this topic within the database domain, current solutions face two major limitations: 1) Both approaches fail to address personalized user requirements in OCS, consistently returning the same set of nodes for a given query regardless of user differences. 2) Existing ML-based CS models suffer from severe training efficiency issues. In this paper, we formally redefine the problem of OCS. By analyzing the gaps in both types of approaches, we then propose a general solution for OCS named Sparse Subspace Filter (SSF), which can extend any ML-based CS model to enable personalized search in overlapping structures. To overcome the efficiency issue in the current models, we introduce Simplified Multi-hop Attention Networks (SMN), a lightweight yet effective community search model with larger receptive fields. To the best of our knowledge, this is the first ML-based study of overlapping community search. Extensive experiments validate the superior performance of SMN within the SSF pipeline, achieving a 13.73% improvement in F1-Score and up to 3 orders of magnitude acceleration in model efficiency compared to state-of-the-art approaches.
title	Deep Overlapping Community Search via Subspace Embedding
topic	Social and Information Networks Databases Physics and Society
url	https://arxiv.org/abs/2404.14692