Detecting the Structure of Social Networks Using (α,β)-Communities

He, Jing; Liang, Hongyu; Suwajanakorn, Supasorn; Wang, Liaoruo

doi:10.1007/978-3-642-21286-4_3

Cited by 11 publications

(12 citation statements)

References 13 publications

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“…"A", "B", and "C" are three elite users identified using the (α, β) algorithm [He et al 2011]. SVM correctly predicts that there is a follow-back link from "C" to "B", but misses predicting the follow-back link from "C" to "A".…”

Section: Qualitative Case Studymentioning

confidence: 99%

“…We categorize users into two groups (elite users and ordinary users) by three different algorithms: PageRank [Page et al 1999] 3 , #degree, and (α, β) algorithm [He et al 2011] 4 . Specifically, with PageRank, we estimate the importance of each user according to the network structure, and then select top 1% users 5 who have the highest PageRank scores as elite users and the rest as ordinary users; while with #degree, we select top 1% users with the highest number of indegree as elite users and the rest as ordinary users.…”

Section: Observationsmentioning

confidence: 99%

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Learning to predict reciprocity and triadic closure in social networks

Lou

Tang

Hopcroft

et al. 2013

ACM Trans. Knowl. Discov. Data

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We study how links are formed in social networks. In particular, we focus on investigating how a reciprocal (two-way) link, the basic relationship in social networks, is developed from a parasocial (one-way) relationship and how the relationships further develop into triadic closure, one of the fundamental processes of link formation.We first investigate how geographic distance and interactions between users influence the formation of link structure among users. Then we study how social theories including homophily, social balance, and social status are satisfied over networks with parasocial and reciprocal relationships. The study unveils several interesting phenomena. For example, "friend's friend is a friend" indeed exists in the reciprocal relationship network, but does not hold in the parasocial relationship network.We propose a learning framework to formulate the problems of predicting reciprocity and triadic closure into a graphical model. We demonstrate that it is possible to accurately infer 90% of reciprocal relationships in a Twitter network. The proposed model also achieves better performance (+20-30% in terms of F1-measure) than several alternative methods for predicting the triadic closure formation.

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Section: Qualitative Case Studymentioning

confidence: 99%

Section: Observationsmentioning

confidence: 99%

Learning to predict reciprocity and triadic closure in social networks

Lou

Tang

Hopcroft

et al. 2013

ACM Trans. Knowl. Discov. Data

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“…IMIn&Known creates a length-2 feature vector containing both of these values. Classification-based AB, BFS, RW, RWR identify 300 communities on each network via the α-β swap algorithm [7], breadth-firstsearch, random walk without restart, and random walk with 15% chance of restart, respectively. For each of these communities, they calculate a length-36 feature vector including statistics such as conductance, diameter, density, etc.…”

Section: Network Similarity Methodsmentioning

confidence: 99%

A Guide to Selecting a Network Similarity Method

Soundarajan¹,

Eliassi-Rad²,

Gallagher³

2014

Proceedings of the 2014 SIAM International Conference on Data Mining

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We consider the problem of determining how similar two networks (without known node-correspondences) are. This problem occurs frequently in real-world applications such as transfer learning and change detection. Many networksimilarity methods exist; and it is unclear how one should select from amongst them. We provide the first empirical study on the relationships between different networksimilarity methods. Specifically, we present (1) an approach for identifying groups of comparable network-similarity methods and (2) an approach for computing the consensus among a given set of network-similarity methods. We compare and contrast twenty network-similarity methods by applying our approaches to a variety of real datasets spanning multiple domains. Our experiments demonstrate that (1) different network-similarity methods are surprisingly well correlated, (2) some complex network-similarity methods can be closely approximated by a much simpler method, and (3) a few network-similarity methods produce rankings that are very close to the consensus ranking.

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“…The problem of community detection has been extensively studied and many algorithms have been proposed, such as cut-and conductance-based methods [4]- [7], spectral clustering [2][8] [9], (α, β)-clustering [10] [11], and topic modeling methods [12]. The cut-and conductance-based and spectral clustering methods are usually based on a fundamental assumption that communities have dense internal connections and sparse external connections.…”

Section: Introductionmentioning

confidence: 99%

Detecting Community Kernels in Large Social Networks

Wang

Lou

Tang

et al. 2011

2011 IEEE 11th International Conference on Data Mining

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In many social networks, there exist two types of users that exhibit different influence and different behavior. For instance, statistics have shown that less than 1% of the Twitter users (e.g. entertainers, politicians, writers) produce 50% of its content [1], while the others (e.g. fans, followers, readers) have much less influence and completely different social behavior.In this paper, we define and explore a novel problem called community kernel detection in order to uncover the hidden community structure in large social networks. We discover that influential users pay closer attention to those who are more similar to them, which leads to a natural partition into different community kernels.We propose GREEDY and WEBA, two efficient algorithms for finding community kernels in large social networks. GREEDY is based on maximum cardinality search, while WEBA formalizes the problem in an optimization framework. We conduct experiments on three large social networks: Twitter, Wikipedia, and Coauthor, which show that WEBA achieves an average 15%-50% performance improvement over the other state-of-the-art algorithms, and WEBA is on average 6-2,000 times faster in detecting community kernels.

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Detecting the Structure of Social Networks Using (α,β)-Communities

Cited by 11 publications

References 13 publications

Learning to predict reciprocity and triadic closure in social networks

Learning to predict reciprocity and triadic closure in social networks

A Guide to Selecting a Network Similarity Method

Detecting Community Kernels in Large Social Networks

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