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Jitao Lu

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3 papers

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TMLR Journal 2024 Journal Article

Hyperspherical Prototype Node Clustering

Jitao Lu
Danyang Wu
Feiping Nie
Rong Wang
Xuelong Li

The general workflow of deep node clustering is to encode the nodes into node embeddings via graph neural networks and uncover clustering decisions from them, so clustering performance is heavily affected by the embeddings. However, existing works only consider preserving the semantics of the graph but ignore the inter-cluster separability of the nodes, so there's no guarantee that the embeddings can present a clear clustering structure. To remedy this deficiency, we propose Hyperspherical Prototype Node Clustering (HPNC), an end-to-end clustering paradigm that explicitly enhances the inter-cluster separability of learned node embeddings. Concretely, we constrain the embedding space to a unit-hypersphere, enabling us to scatter the cluster prototypes over the space with maximized pairwise distances. Then, we employ a graph autoencoder to map nodes onto the same hypersphere manifold. Consequently, cluster affinities can be directly retrieved from cosine similarities between node embeddings and prototypes. A clustering-oriented loss is imposed to sharpen the affinity distribution so that the learned node embeddings are encouraged to have small intra-cluster distances and large inter-cluster distances. Based on the proposed HPNC paradigm, we devise two schemes (HPNC-IM and HPNC-DEC) with distinct clustering backbones. Empirical results on popular benchmark datasets demonstrate the superiority of our method compared to other state-of-the-art clustering methods, and visualization results illustrate improved separability of the learned embeddings.

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IJCAI Conference 2022 Conference Paper

EMGC²F: Efficient Multi-view Graph Clustering with Comprehensive Fusion

Danyang Wu
Jitao Lu
Feiping Nie
Rong Wang
Yuan Yuan

This paper proposes an Efficient Multi-view Graph Clustering with Comprehensive Fusion (EMGC²F) model and a corresponding efficient optimization algorithm to address multi-view graph clustering tasks effectively and efficiently. Compared to existing works, our proposals have the following highlights: 1) EMGC²F directly finds a consistent cluster indicator matrix with a Super Nodes Similarity Minimization module from multiple views, which avoids time-consuming spectral decomposition in previous works. 2) EMGC²F comprehensively mines information from multiple views. More formally, it captures the consistency of multiple views via a Cross-view Nearest Neighbors Voting (CN²V) mechanism, meanwhile capturing the importance of multiple views via an adaptive weighted-learning mechanism. 3) EMGC²F is a parameter-free model and the time complexity of the proposed algorithm is far less than existing works, demonstrating the practicability. Empirical results on several benchmark datasets demonstrate that our proposals outperform SOTA competitors both in effectiveness and efficiency.

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IJCAI Conference 2021 Conference Paper

Discrete Multiple Kernel k-means

Rong Wang
Jitao Lu
Yihang Lu
Feiping Nie
Xuelong Li

The multiple kernel k-means (MKKM) and its variants utilize complementary information from different kernels, achieving better performance than kernel k-means (KKM). However, the optimization procedures of previous works all comprise two stages, learning the continuous relaxed label matrix and obtaining the discrete one by extra discretization procedures. Such a two-stage strategy gives rise to a mismatched problem and severe information loss. To address this problem, we elaborate a novel Discrete Multiple Kernel k-means (DMKKM) model solved by an optimization algorithm that directly obtains the cluster indicator matrix without subsequent discretization procedures. Moreover, DMKKM can strictly measure the correlations among kernels, which is capable of enhancing kernel fusion by reducing redundancy and improving diversity. What’s more, DMKKM is parameter-free avoiding intractable hyperparameter tuning, which makes it feasible in practical applications. Extensive experiments illustrated the effectiveness and superiority of the proposed model.

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