Local Graph Clustering by Multi-network Random Walk with Restart

Yan, Yaowei; Luo, Dongsheng; Ni, Jingchao; Fei, Hongliang; Wang, Fan; Yu, Xiong; Yen, John; Zhang, Xiang

doi:10.1007/978-3-319-93040-4_39

Cited by 5 publications

(7 citation statements)

References 22 publications

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“…Further, both ω and r are set manually and stay the same for all nodes, which means these two methods only consider the global relationship between networks, which limits their applications in general and complex networks. In [58], the authors propose a random walk method on multi-domain networks by including a transition matrix between different networks. However, the transition matrix between networks is set manually and is independent of the starting node(s).…”

Section: Related Workmentioning

confidence: 99%

Random Walk on Multiple Networks

Luo

Bian

Yan

et al. 2022

IEEE Trans. Knowl. Data Eng.

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Random Walk is a basic algorithm to explore the structure of networks, which can be used in many tasks, such as local community detection and network embedding. Existing random walk methods are based on single networks that contain limited information. In contrast, real data often contain entities with different types or/and from different sources, which are comprehensive and can be better modeled by multiple networks. To take the advantage of rich information in multiple networks and make better inferences on entities, in this study, we propose random walk on multiple networks, RWM. RWM is flexible and supports both multiplex networks and general multiple networks, which may form many-to-many node mappings between networks. RWM sends a random walker on each network to obtain the local proximity (i.e., node visiting probabilities) w.r.t. the starting nodes. Walkers with similar visiting probabilities reinforce each other. We theoretically analyze the convergence properties of RWM. Two approximation methods with theoretical performance guarantees are proposed for efficient computation. We apply RWM in link prediction, network embedding, and local community detection. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness and efficiency of RWM.

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Section: Related Workmentioning

confidence: 99%

Random Walk on Multiple Networks

Luo

Bian

Yan

et al. 2022

IEEE Trans. Knowl. Data Eng.

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“…A recent survey about local community detection and community search can be found in [10]. Except for that, random walk based methods have also been routinely applied to detect local communities in a single network [2,5,8,15,32,34,35]. A walker explores the network following the topological transitions.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, a modified random walk model is proposed in [8] for multiplex networks. For the general multi-domain networks, the method in [34] can only detect one local community in the query network domain. More importantly, these methods assume that all networks share similar or consistent structures.…”

Section: Related Workmentioning

confidence: 99%

“…To find the local community in network G i , we follow the common process in the literature [2,5,15,34]. We first sort nodes according to the converged score vector x (T ) i , where T is the number of iterations.…”

Section: Local Community Detection With Restart Strategymentioning

confidence: 99%

“…ML-LCD [12] uses a greedy strategy to find the local communities on multiplex networks with the same node set. MRWR [34] only focuses on the query network. Besides, without prior knowledge, MRWR treats other networks contributing equally.…”

Section: Datasets and Baselinesmentioning

confidence: 99%

See 2 more Smart Citations

Local Community Detection in Multiple Networks

Luo

Bian

Yan

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Local community detection aims to find a set of densely-connected nodes containing given query nodes. Most existing local community detection methods are designed for a single network. However, a single network can be noisy and incomplete. Multiple networks are more informative in real-world applications. There are multiple types of nodes and multiple types of node proximities. Complementary information from different networks helps to improve detection accuracy. In this paper, we propose a novel RWM (Random W alk in Multiple networks) model to find relevant local communities in all networks for a given query node set from one network. RWM sends a random walker in each network to obtain the local proximity w.r.t. the query nodes (i.e., node visiting probabilities). Walkers with similar visiting probabilities reinforce each other. They restrict the probability propagation around the query nodes to identify relevant subgraphs in each network and disregard irrelevant parts. We provide rigorous theoretical foundations for RWM and develop two speeding-up strategies with performance guarantees. Comprehensive experiments are conducted on synthetic and real-world datasets to evaluate the effectiveness and efficiency of RWM.

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Community Search over Big Graphs

Huang

Lakshmanan

2019

Synthesis Lectures on Data Management

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Local Graph Clustering by Multi-network Random Walk with Restart

Cited by 5 publications

References 22 publications

Random Walk on Multiple Networks

Random Walk on Multiple Networks

Local Community Detection in Multiple Networks

Community Search over Big Graphs

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