Dual-channel attribute graph clustering beyond the homogeneity assumption
In recent years, significant progress has been made in the research of attribute graph clustering. However, existing methods are mostly based on the homogeneity assumption, thereby neglecting the application scenarios of heterogeneous graphs, leading to the loss of high-frequency information and poo...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2025-01-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025009/ |
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| _version_ | 1823864707680829440 |
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| author | AN Junxiu LIU Yuan YANG Linwang |
| author_facet | AN Junxiu LIU Yuan YANG Linwang |
| author_sort | AN Junxiu |
| collection | DOAJ |
| description | In recent years, significant progress has been made in the research of attribute graph clustering. However, existing methods are mostly based on the homogeneity assumption, thereby neglecting the application scenarios of heterogeneous graphs, leading to the loss of high-frequency information and poor clustering results during the clustering process. To address this issue, a novel dual-channel attribute graph clustering (DCAGC) method was proposed. A mixture of Gaussian models was used to predict the homogeneity of node connections and two views of homogeneous and heterogeneous were built, based on this prediction to capture low-frequency and high-frequency information in the graph from different perspectives. Simultaneously, by integrating contrastive learning and clustering, more precise node embeddings were achieved. Compared to other methods, DCAGC demonstrates significant clustering performance when handling heterogeneous graph datasets and exhibits strong resilience to anomalous connections. |
| format | Article |
| id | doaj-art-ad11da803fff4016b8d3965d7cc1b5ea |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-ad11da803fff4016b8d3965d7cc1b5ea2025-02-08T19:00:26ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012025-01-014111112482012005Dual-channel attribute graph clustering beyond the homogeneity assumptionAN JunxiuLIU YuanYANG LinwangIn recent years, significant progress has been made in the research of attribute graph clustering. However, existing methods are mostly based on the homogeneity assumption, thereby neglecting the application scenarios of heterogeneous graphs, leading to the loss of high-frequency information and poor clustering results during the clustering process. To address this issue, a novel dual-channel attribute graph clustering (DCAGC) method was proposed. A mixture of Gaussian models was used to predict the homogeneity of node connections and two views of homogeneous and heterogeneous were built, based on this prediction to capture low-frequency and high-frequency information in the graph from different perspectives. Simultaneously, by integrating contrastive learning and clustering, more precise node embeddings were achieved. Compared to other methods, DCAGC demonstrates significant clustering performance when handling heterogeneous graph datasets and exhibits strong resilience to anomalous connections.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025009/attribute graph clusteringself-supervised learningheterogeneous graph learning |
| spellingShingle | AN Junxiu LIU Yuan YANG Linwang Dual-channel attribute graph clustering beyond the homogeneity assumption Dianxin kexue attribute graph clustering self-supervised learning heterogeneous graph learning |
| title | Dual-channel attribute graph clustering beyond the homogeneity assumption |
| title_full | Dual-channel attribute graph clustering beyond the homogeneity assumption |
| title_fullStr | Dual-channel attribute graph clustering beyond the homogeneity assumption |
| title_full_unstemmed | Dual-channel attribute graph clustering beyond the homogeneity assumption |
| title_short | Dual-channel attribute graph clustering beyond the homogeneity assumption |
| title_sort | dual channel attribute graph clustering beyond the homogeneity assumption |
| topic | attribute graph clustering self-supervised learning heterogeneous graph learning |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025009/ |
| work_keys_str_mv | AT anjunxiu dualchannelattributegraphclusteringbeyondthehomogeneityassumption AT liuyuan dualchannelattributegraphclusteringbeyondthehomogeneityassumption AT yanglinwang dualchannelattributegraphclusteringbeyondthehomogeneityassumption |