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20
Exploiting Longer Cycles for Link Prediction in Signed Networks
, 2011
"... We consider the problem of link prediction in signed networks. Such networks arise on the web in a variety of ways when users can implicitly or explicitly tag their relationship with other users as positive or negative. The signed links thus created reflect social attitudes of the users towards each ..."
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Cited by 15 (2 self)
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We consider the problem of link prediction in signed networks. Such networks arise on the web in a variety of ways when users can implicitly or explicitly tag their relationship with other users as positive or negative. The signed links thus created reflect social attitudes of the users towards each other in terms of friendship or trust. Our first contribution is to show how any quantitative measure of social imbalance in a network can be used to derive a link prediction algorithm. Our framework allows us to reinterpret some existing algorithms as well as derive new ones. Second, we extend the approach of [6], by presenting a supervised machine learning based link prediction method that uses features derived from longer cycles in the network. The supervised method outperforms all previous approaches on 3 networks drawn from sources such as Epinions, Slashdot and Wikipedia. The supervised approach easily scales to these networks, the largest of which has 132k nodes and 841k edges. Most real-world networks have an overwhelmingly large proportion of positive edges and it is therefore easy to get a high overall accuracy at the cost of a high false positive rate. We see that our supervised method not only achieves good accuracy for sign prediction but is also especially effective in lowering the false positive rate.
Clustering with Multi-Layer Graphs: A Spectral Perspective
, 2011
"... Observational data usually comes with a multimodal nature, which means that it can be naturally represented by a multi-layer graph whose layers share the same set of vertices (users) with different edges (pairwise relationships). In this paper, we address the problem of combining different layers of ..."
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Cited by 8 (1 self)
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Observational data usually comes with a multimodal nature, which means that it can be naturally represented by a multi-layer graph whose layers share the same set of vertices (users) with different edges (pairwise relationships). In this paper, we address the problem of combining different layers of the multi-layer graph for improved clustering of the vertices compared to using layers independently. We propose two novel methods, which are based on joint matrix factorization and graph regularization framework respectively, to efficiently combine the spectrum of the multiple graph layers, namely the eigenvectors of the graph Laplacian matrices. In each case, the resulting combination, which we call a “joint spectrum ” of multiple graphs, is used for clustering the vertices. We evaluate our approaches by simulations with several real world social network datasets. Results demonstrate the superior or competitive performance of the proposed methods over state-of-the-art technique and common baseline methods, such as co-regularization [1] and summation of information from individual graphs.
Transferring Heterogeneous Links across Location-Based Social Networks
"... Location-based social networks (LBSNs) are one kind of online social networks offering geographic services and have been attracting much attention in recent years. LBSNs usually have complex structures, involving heterogeneous nodes and links. Many recommendation services in LBSNs (e.g., friend and ..."
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Cited by 8 (4 self)
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Location-based social networks (LBSNs) are one kind of online social networks offering geographic services and have been attracting much attention in recent years. LBSNs usually have complex structures, involving heterogeneous nodes and links. Many recommendation services in LBSNs (e.g., friend and location recommendation) can be cast as link prediction problems (e.g., social link and location link prediction). Traditional link prediction researches on LBSNs mostly focus on predicting either social links or location links, assuming the prediction tasks of different types of links to be independent. However, in many real-world LBSNs, the prediction tasks for social links and location links are strongly correlated and mutually influential. Another key challenge in link prediction on LBSNs is the data sparsity problem (i.e., “new network ” problem), which can be encountered when LBSNs branch into new geographic areas or social groups. Actually, nowadays, many users are involved in multiple networks simultaneously and users who just join one LBSN may have been using other LBSNs for a long time. In this paper, we study the problem of predicting multiple types of links simultaneously for a new LBSN across partially aligned LBSNs and propose a novel method TRAIL (TRAnsfer heterogeneous lInks across LBSNs). TRAIL can accumulate information for locations from online posts and extract heterogeneous features for both social links and location links. TRAIL can predict multiple types of links simultaneously. In addition, TRAIL can transfer information from other aligned networks to the new network to solve the problem of lacking information. Extensive experiments conducted on two real-world aligned LBSNs show that TRAIL can achieve very good performance and substantially outperform the baseline methods.
Meta-path based Multi-Network Collective Link Prediction
"... Online social networks o↵ering various services have become ubiquitous in our daily life. Meanwhile, users nowadays are usually involved in multiple online social networks simulta-neously to enjoy specific services provided by di↵erent net-works. Formally, social networks that share some common user ..."
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Cited by 6 (3 self)
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Online social networks o↵ering various services have become ubiquitous in our daily life. Meanwhile, users nowadays are usually involved in multiple online social networks simulta-neously to enjoy specific services provided by di↵erent net-works. 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) pre-diction problem. In multiple partially aligned social net-works, users can be extensively correlated with each other by various connections. To categorize these diverse connec-tions among users, 7 “intra-network social meta paths ” and 4 categories of “inter-network social meta paths ” are pro-
Bistability Through Triadic Closure
, 2011
"... We propose and analyse a class of evolving network models suitable for describing a dynamic topological structure. Applications include telecommunication, on-line social behaviour and information processing in neuroscience. We model the evolving network as a discrete time Markov chain, and study a v ..."
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Cited by 5 (2 self)
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We propose and analyse a class of evolving network models suitable for describing a dynamic topological structure. Applications include telecommunication, on-line social behaviour and information processing in neuroscience. We model the evolving network as a discrete time Markov chain, and study a very general framework where, conditioned on the current state, edges appear or disappear independently at the next timestep. We show how to exploit symmetries in the microscopic, localized rules in order to obtain conjugate classes of random graphs that simplify analysis and calibration of a model. Further, we develop a mean field theory for describing network evolution. For a simple but realistic scenario incorporating the triadic closure effect that has been empirically observed by social scientists (friends of friends tend to become friends), the mean field theory predicts bistable dynamics, and computational results confirm this prediction. We also discuss the calibration issue for a set of real cell phone data, and find support for a stratified model, where individuals are assigned to one of two distinct groups having different within-group and across-group dynamics.
Predicting social links for new users across aligned heterogeneous social networks
, 2013
"... Abstract—Nowadsys, many new users are keeping joining in the online social networks every day and these new users usually have very few social connections and very sparse auxiliary information in the network. Prediction social links for new users is very important. Different from conventional link p ..."
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Cited by 5 (3 self)
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Abstract—Nowadsys, many new users are keeping joining in the online social networks every day and these new users usually have very few social connections and very sparse auxiliary information in the network. Prediction social links for new users is very important. Different from conventional link prediction problems, link prediction for new users is more challenging due to the lack of information from the new users in the network. Meanwhile, in recent years, users are usually involved in multiple social networks simultaneously to enjoy the specific services offered by different social networks. The shared users of multiple networks can act as the “anchors ” aligned the networks they participate in. In this paper, we propose a link prediction method called SCAN-PS (Supervised Cross Aligned Networks link prediction with Personalized Sampling), to solve the social link prediction problem for new users. SCAN-PS can use information transferred from both the existing active users in the target network and other source networks through aligned accounts. In addition, SCAN-PS could solve the cold start problem when information of these new users is total absent in the target network. Extensive experiments conducted on two real-world aligned heterogeneous social networks demonstrate that SCAN-PS can perform well in predicting social links for new users. Index Terms—link prediction; data mining; I.
An evolutionary algorithm approach to link prediction in dynamic social networks
- Journal of Computational Science
, 2014
"... Many real world, complex phenomena have an underlying structure of evolving networks where nodes and links are added and removed over time. A central scientific challenge is the description and explanation of network dynamics, with a key test being the prediction of short and long term changes. For ..."
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Cited by 4 (2 self)
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Many real world, complex phenomena have an underlying structure of evolving networks where nodes and links are added and removed over time. A central scientific challenge is the description and explanation of network dynamics, with a key test being the prediction of short and long term changes. For the problem of short-term link prediction, existing methods attempt to determine neighborhood metrics that correlate with the appearance of a link in the next observation period. Recent work has suggested that the incorporation of user-specific metadata and usage patterns can improve link prediction, however methodologies for doing so in a systematic way are largely unexplored in the literature. Here, we provide a novel approach to predicting future links by applying an evolutionary algorithm (Covariance Matrix Adaptation Evolution Strategy) to weights which are used in a linear combination of sixteen neighborhood and node similarity indices. We examine Twitter reciprocal reply networks constructed at the time scale of weeks, both as a test of our general
Pseudo Cold Start Link Prediction with Multiple Sources in Social Networks
"... Link prediction is an important task in social networks and data mining for understanding the mechanisms by which the social networks form and evolve. In most link prediction researches, it is assumed either a snapshot of the social network or a social network with some missing links is available. M ..."
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Cited by 2 (0 self)
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Link prediction is an important task in social networks and data mining for understanding the mechanisms by which the social networks form and evolve. In most link prediction researches, it is assumed either a snapshot of the social network or a social network with some missing links is available. Most existing researches therefore approach this problem by exploring the topological structure of the social network using only one source of information. However, in many application domains, in addition to the social network of interest, there are a number of auxiliary information available. In this work, we introduce the pseudo cold start link prediction with multiple sources as the problem of predicting the structure of a social network when only a small subgraph of the social network is known and multiple heterogeneous sources are available. We propose a two-phase supervised method: the first phase generates an efficient feature selection scheme to find the best feature from multiple sources thatisusedforpredictingthestructureinthesocialnetwork. In the second phase, we propose a regularization method to control the risk of over-fitting induced by the first phase. We assess our method empirically over a large data collection obtained from Youtube. The extensive experimental evaluations confirm the effectiveness of our approach. 1
Reciprocal and Heterogeneous Link Prediction in Social Networks
"... Abstract. Link prediction is a key technique in many applications in social networks, where potential links between entities need to be predicted. Conventional link prediction techniques deal with either homogeneous entities, e.g., people to people, item to item links, or non-reciprocal relationship ..."
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Cited by 2 (0 self)
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Abstract. Link prediction is a key technique in many applications in social networks, where potential links between entities need to be predicted. Conventional link prediction techniques deal with either homogeneous entities, e.g., people to people, item to item links, or non-reciprocal relationships, e.g., people to item links. However, a challenging problem in link prediction is that of heterogeneous and reciprocal link prediction, such as accurate prediction of matches on an online dating site, jobs or workers on employment websites, where the links are reciprocally determined by both entities that heterogeneously belong to disjoint groups. The nature and causes of interactions in these domains makes heterogeneous and reciprocal link prediction significantly different from the conventional version of the problem. In this work, we address these issues by proposing a novel learnable framework called ReHeLP, which learns heterogeneous and reciprocal knowledge from collaborative information and demonstrate its impact on link prediction. Evaluation on a large commercial online dating dataset shows the success of the proposed method and its promise for link prediction.
