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Abstract: Heterogeneous information networks (HINs) with rich semantics are ubiquitousin real-world applications. For a given HIN, many reasonable clustering resultswith distinct semantic meaning can simultaneously exist. User-guided clusteringis hence of great practical value for HINs where users provide labels to asmall portion of nodes. To cater to a broad spectrum of user guidance evidencedby different expected clustering results, carefully exploiting the signalsresiding in the data is potentially useful. Meanwhile, as one type of complexnetworks, HINs often encapsulate higher-order interactions that reflect theinterlocked nature among nodes and edges. Network motifs, sometimes referred toas meta-graphs, have been used as tools to capture such higher-orderinteractions and reveal the many different semantics. We therefore approach theproblem of user-guided clustering in HINs with network motifs. In this process,we identify the utility and importance of directly modeling higher-orderinteractions without collapsing them to pairwise interactions. To achieve this,we comprehensively transcribe the higher-order interaction signals to a seriesof tensors via motifs and propose the MoCHIN model based on joint non-negativetensor factorization. This approach applies to arbitrarily many, arbitraryforms of HIN motifs. An inference algorithm with speed-up methods is alsoproposed to tackle the challenge that tensor size grows exponentially as thenumber of nodes in a motif increases. We validate the effectiveness of theproposed method on two real-world datasets and three tasks, and MoCHINoutperforms all baselines in three evaluation tasks under three differentmetrics. Additional experiments demonstrated the utility of motifs and thebenefit of directly modeling higher-order information especially when userguidance is limited.
Submission history
From: Yu Shi [view email][v1] Wed, 28 Nov 2018 00:16:03 UTC (614 KB)
[v2] Thu, 27 Jun 2019 02:51:29 UTC (1,509 KB)[v3]
Sun, 22 Sep 2019 22:39:09 UTC (1,048 KB)Full-text links:
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DBLP - CS Bibliography
Xinwei He
Naijing Zhang
Carl Yang
Jiawei Han
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