Social networks contain implicit knowledge that can be used to infer hierarchical relations that are not explicitly present in the available data. Interaction patterns are typically affected by use ...
Finally, link prediction plays an essential role in discovering interactions within social networks. Along this line, it draws an immense interest in the field of data mining and networks communication. The link prediction problem is one of the underlying problems in social network evaluation. It has been used in different contexts, for instance, in companies to discover the interactions within social networks @cite_11 , even in security sector by monitoring terrorist networks @cite_18 or to be used in prediction of missing links in a community.
@cite_18 @cite_11
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The problem of link prediction has been studied extensively in literature. There are various versions of the link prediction problem link existence problem, link removal problem, predicting edge weights over time etc. In this paper we describe a new type of link prediction problem called the Inter-network link-prediction problem where the task is to predict links different networks. Thus given a set of nodes which participate in multiple networks the task is to determine if one can predict the edges that occur in one network by only using node attribute and edge information from other networks. We use insights from theories of evolution of social communication networks and the MTML framework to derive models which can be used to make link predictions across networks. For the experiments data from different of social networks from a Massively Multiplayer Online Role Playing Game (MMORPG) is used. Link prediction, i.e., predicting links or interactions between objects in a network, is an important task in network analysis. Although the problem has attracted much attention recently, there are several challenges that have not been addressed so far. First, most existing studies focus only on link prediction in homogeneous networks, where all objects and links belong to the same type. However, in the real world, heterogeneous networks that consist of multi-typed objects and relationships are ubiquitous. Second, most current studies only concern the problem of whether a link will appear in the future but seldom pay attention to the problem of when it will happen. In this paper, we address both issues and study the problem of predicting when a certain relationship will happen in the scenario of heterogeneous networks. First, we extend the link prediction problem to the relationship prediction problem, by systematically defining both the target relation and the topological features, using a meta path-based approach. Then, we directly model the distribution of relationship building time with the use of the extracted topological features. The experiments on citation relationship prediction between authors on the DBLP network demonstrate the effectiveness of our methodology. Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures. Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open. We develop an algorithm based on Supervised Random Walks that naturally combines the information from the network structure with node and edge level attributes. We achieve this by using these attributes to guide a random walk on the graph. We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links will be created in the future. We develop an efficient training algorithm to directly learn the edge strength estimation function. Our experiments on the Facebook social graph and large collaboration networks show that our approach outperforms state-of-the-art unsupervised approaches as well as approaches that are based on feature extraction.