Community detection in incomplete information networks

Lin, Wangqun; Kong, Xiangnan; Yu, Philip S.; Wu, Quanyuan; Jia, Yan; Li, Chuan

doi:10.1145/2187836.2187883

Cited by 85 publications

(44 citation statements)

References 34 publications

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“…For example, Lin et al [Lin et al 2012] proposed a method for community detection, based on graph clustering, in networks with incomplete information. In these networks, the links within a few local regions are known, but links from the entire network are missing.…”

Section: Related Workmentioning

confidence: 99%

“…We assume that a set ratio exists between nodes within the network and their neighbors. This relationship has been previously studied within missing link and node literature [Gomez-Rodriguez et al 2012;Lin et al 2012;Kim and Leskovec 2011]. We considered two methods, quadratic and mean estimations.…”

Section: Estimating the Number Of Missing Nodesmentioning

confidence: 99%

“…One important factor that is often studied is the structure of these networks [Clauset et al 2008;Eslami et al 2011;Fortunato 2010;Freno et al ;Gomez-Rodriguez et al 2012;Gong et al 2011;Kim and Leskovec 2011;Leroy et al 2010;Liben-Nowell and Kleinberg 2007;Lin et al 2012;Porter et al 2009;Sadikov et al 2011]. Previously, a Link Prediction Problem [Clauset et al 2008;Liben-Nowell and Kleinberg 2007] was defined as attempting to locate which connections (edges) will soon exist between nodes.…”

Section: Introductionmentioning

confidence: 99%

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Predicting and Identifying Missing Node Information in Social Networks

Eyal

Rosenfeld

Sina

et al. 2013

ACM Trans. Knowl. Discov. Data

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In recent years, social networks have surged in popularity. One key aspect of social network research is identifying important missing information which is not explicitly represented in the network, or is not visible to all. To date, this line of research typically focused on finding the connections that are missing between nodes, a challenge typically termed as the Link Prediction Problem. This paper introduces the Missing Node Identification problem where missing members in the social network structure must be identified. In this problem, indications of missing nodes are assumed to exist. Given these indications and a partial network, we must assess which indications originate from the same missing node and determine the full network structure.Towards solving this problem, we present the MISC Algorithm (Missing node Identification by Spectral Clustering), an approach based on a spectral clustering algorithm, combined with nodes' pairwise affinity measures which were adopted from link prediction research. We evaluate the performance of our approach in different problem settings and scenarios, using real life data from Facebook. The results show that our approach has beneficial results and can be effective in solving the Missing Node Identification Problem. In addition, this paper also presents R-MISC which uses a sparse matrix representation, efficient algorithms for calculating the nodes' pairwise affinity and a proprietary dimension reduction technique, to enable scaling the MISC algorithm to large networks of more than 100,000 nodes. Last, we consider problem settings where some of the indications are unknown. Two algorithms are suggested for this problem -Speculative MISC, based on MISC, and Missing Link Completion, based on classical link prediction literature. We show that Speculative MISC outperforms Missing Link Completion.

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Section: Related Workmentioning

confidence: 99%

Section: Estimating the Number Of Missing Nodesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting and Identifying Missing Node Information in Social Networks

Eyal

Rosenfeld

Sina

et al. 2013

ACM Trans. Knowl. Discov. Data

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“…Scientists in both academia and industry have recognized the importance of these networks and have focused on various aspects of social networks. One aspect that is often studied is the structure of these networks [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]. Previously, a missing link problem [1], [2] was defined as attempting to locate which connections (edges) will soon exist between nodes.…”

Section: Introductionmentioning

confidence: 99%

Solving the missing node problem using structure and attribute information

Sina

Rosenfeld

Kraus

2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Abstract-An important area of social networks research is identifying missing information which is not explicitly represented in the network, or is not visible to all. Recently, the Missing Node Identification problem was introduced where missing members in the social network structure must be identified. However, previous works did not consider the possibility that information about specific users (nodes) within the network could be useful in solving this problem. In this paper, we present two algorithms: SAMI-A and SAMI-N. Both of these algorithms use the known nodes' specific information, such as demographic information and the nodes' historical behavior in the network. We found that both SAMI-A and SAMI-N perform significantly better than other missing node algorithms. However, as each of these algorithms and the parameters within these algorithms often perform better in specific problem instances, a mechanism is needed to select the best algorithm and the best variation within that algorithm. Towards this challenge, we also present OASCA, a novel online selection algorithm. We present results that detail the success of the algorithms presented within this paper.

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“…The literature on algorithms for inference of network structure from a sample is growing, and currently includes work on inference of missing nodes, edges and even community structure [55][56][57].…”

Section: Application: Null Analysis Of Empirical Networkmentioning

confidence: 99%

Exploring community structure in biological networks with random graphs

Sah

Singh

Clauset

et al. 2013

Preprint

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Background: Community structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system's functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge. Results: Here, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks. Conclusion: Our model allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems.

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Community detection in incomplete information networks

Cited by 85 publications

References 34 publications

Predicting and Identifying Missing Node Information in Social Networks

Predicting and Identifying Missing Node Information in Social Networks

Solving the missing node problem using structure and attribute information

Exploring community structure in biological networks with random graphs

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