Community detection in online social networks has been a hot research topic in recent years. Meanwhile, to enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously, some of which can share common information and structures. Networks that involve some common users are named as multiple "partially aligned networks". In this paper, we want to detect communities of multiple partially aligned networks simultaneously, which is formally defined as the "Mutual Clustering" problem. The "Mutual Clustering" problem is very challenging as it has two important issues to address: (1) how to preserve the network characteristics in mutual community detection? and (2) how to utilize the information in other aligned networks to refine and disambiguate the community structures of the shared users? To solve these two challenges, a novel community detection method, MCD (Mutual Community Detector), is proposed in this paper. MCD can detect social community structures of users in multiple partially aligned networks at the same time with full considerations of (1) characteristics of each network, and (2) information of the shared users across aligned networks. Extensive experiments conducted on two real-world partially aligned heterogeneous social networks demonstrate that MCD can solve the "Mutual Clustering" problem very well.
In recent years, researchers' attention has started to shift to study multiple heterogeneous social networks simultaneously. @cite_5 are the first to propose the concepts of and . Across aligned social networks, different social network application problems have been studied, which include different cross-network link prediction transfer @cite_42 @cite_23 @cite_44 @cite_12 , emerging network clustering @cite_27 and large-scale network community detection @cite_15 , inter-network information diffusion and influence maximization @cite_16 .
@cite_42 @cite_44 @cite_27 @cite_23 @cite_5 @cite_15 @cite_16 @cite_12
2022580894 1976160665 2499358516 2047532797
Online social networks offering various services have become ubiquitous in our daily life. Meanwhile, users nowadays are usually involved in multiple online social networks simultaneously to enjoy specific services provided by different networks. Formally, social networks that share some common users are named as partially aligned networks. In this paper, we want to predict the formation of social links in multiple partially aligned social networks at the same time, which is formally defined as the multi-network link (formation) prediction problem. In multiple partially aligned social networks, users can be extensively correlated with each other by various connections. To categorize these diverse connections among users, 7 "intra-network social meta paths" and 4 categories of "inter-network social meta paths" are proposed in this paper. These "social meta paths" can cover a wide variety of connection information in the network, some of which can be helpful for solving the multi-network link prediction problem but some can be not. To utilize useful connection, a subset of the most informative "social meta paths" are picked, the process of which is formally defined as "social meta path selection" in this paper. An effective general link formation prediction framework, Mli (Multi-network Link Identifier), is proposed in this paper to solve the multi-network link (formation) prediction problem. Built with heterogenous topological features extracted based on the selected "social meta paths" in the multiple partially aligned social networks, Mli can help refine and disambiguate the prediction results reciprocally in all aligned networks. Extensive experiments conducted on real-world partially aligned heterogeneous networks, Foursquare and Twitter, demonstrate that Mli can solve the multi-network link prediction problem very well. Graph clustering has been widely applied in exploring regularities emerging in relational data. Recently, the rapid development of network theory correlates graph clustering with the detection of community structure, a common and important topological characteristic of networks. Most existing methods investigate the community structure at a single topological scale. However, as shown by empirical studies, the community structure of real world networks often exhibits multiple topological descriptions, corresponding to the clustering at different resolutions. Furthermore, the detection of multiscale community structure is heavily affected by the heterogeneous distribution of node degree. It is very challenging to detect multiscale community structure in heterogeneous networks. In this paper, we propose a novel, unified framework for detecting community structure from the perspective of dimensionality reduction. Based on the framework, we first prove that the well-known Laplacian matrix for network partition and the widely-used modularity matrix for community detection are two kinds of covariance matrices used in dimensionality reduction. We then propose a novel method to detect communities at multiple topological scales within our framework. We further show that existing algorithms fail to deal with heterogeneous node degrees. We develop a novel method to handle heterogeneity of networks by introducing a rescaling transformation into the covariance matrices in our framework. Extensive tests on real world and artificial networks demonstrate that the proposed correlation matrices significantly outperform Laplacian and modularity matrices in terms of their ability to identify multiscale community structure in heterogeneous networks. Online social networks have gained great success in recent years. Some online social networks only involving users and social links among users can be represented as homogeneous networks. Meanwhile, some other social networks containing abundant information, which include multiple kinds of nodes and complex relationships, can be denoted as heterogeneous networks. Predicting the missing links or links that will be formed in the future based on a snapshot of social networks is formally defined as the link prediction problem. Link prediction problems have extensive applications in real-world social networks and many concrete social services can be cast as link prediction tasks, e.g., friend and location recommendations can all be solved as the problem of predicting social links among users and the location links between users and locations. Link prediction problems have been an important research topic for many years and a large number of different methods have been proposed so far. This article summaries the existing link prediction methods for both homogeneous and heterogeneous networks, which include various unsupervised link predicators, random walk based link prediction methods, methods based on matrix factorization techniques, supervised link prediction methods and meta paths based link prediction methods. Meanwhile, as proposed in recent works, people are usually involved in multiple social networks simultaneously nowadays and networks sharing common users are formally defined as the aligned networks. In this article, we will also introduce the latest progress of link prediction problems across multiple aligned heterogeneous networks. The link prediction problems across aligned networks can include anchor link prediction problem and link transfer across aligned heterogeneous networks. We will introduce the newly proposed methods to solve these problems in details and, finally, we will conclude this survey with a discussion about the future link prediction research works. Online social networks can often be represented as heterogeneous information networks containing abundant information about: who, where, when and what. Nowadays, people are usually involved in multiple social networks simultaneously. The multiple accounts of the same user in different networks are mostly isolated from each other without any connection between them. Discovering the correspondence of these accounts across multiple social networks is a crucial prerequisite for many interesting inter-network applications, such as link recommendation and community analysis using information from multiple networks. In this paper, we study the problem of anchor link prediction across multiple heterogeneous social networks, i.e., discovering the correspondence among different accounts of the same user. Unlike most prior work on link prediction and network alignment, we assume that the anchor links are one-to-one relationships (i.e., no two edges share a common endpoint) between the accounts in two social networks, and a small number of anchor links are known beforehand. We propose to extract heterogeneous features from multiple heterogeneous networks for anchor link prediction, including user's social, spatial, temporal and text information. Then we formulate the inference problem for anchor links as a stable matching problem between the two sets of user accounts in two different networks. An effective solution, MNA (Multi-Network Anchoring), is derived to infer anchor links w.r.t. the one-to-one constraint. Extensive experiments on two real-world heterogeneous social networks show that our MNA model consistently outperform other commonly-used baselines on anchor link prediction.