Link Prediction in Social Networks Using Computationally Efficient Topological Features

Abstract

Abstract-Online social networking sites have become increasingly popular over the last few years. As a result, new interdisciplinary research directions have emerged in which social network analysis methods are applied to networks containing hundreds millions of users. Unfortunately, links between individuals may be missing either due to imperfect acquirement processes or because they are not yet reflected in the online network (i.e., friends in real-world did not form a virtual connection.) Existing link prediction techniques lack the scalability required for full application on a continuously growing social network.The primary bottleneck in link prediction techniques is extracting structural features required for classifying links. In this paper we propose a set of simple, easy-to-compute structural features, that can be analyzed to identify missing links. We show that by using simple structural features, a machine learning classifier can successfully identify missing links, even when applied to a hard problem of classifying links between individuals with at least one common friend. A new friends measure that we developed is shown to be a good predictor for missing links. An evaluation experiment was performed on five large Social Networks datasets: Facebook, Flickr, YouTube, Academia and TheMarker. Our methods can provide social network site operators with the capability of helping users to find known, offline contacts and to discover new friends online. They may also be used for exposing hidden links in an online social network.

Link Prediction in Social Networks based on Spectral Clustering using k-medoids and Landmark

“…Step 6. Based on triangle inequality calculate the similarity between nodes that belong in the same cluster using Equation (5) and the similarity between nodes that belong to different clusters using Equation (6), higher similarity means higher probability that corresponding nodes are connected.…”

“…Social network is dynamically important, because new edges and vertices are added to the graph over time. To understand the association between two specific nodes, link prediction is discussed where link prediction focus on predicting the existent edge between two nodes that will probably occur in the near future [4] [5].…”

Link Prediction in Microblog Network Using Supervised Learning with Multiple Features

“…Traditional link prediction methods always consider the vertical, link information and the topological features [4], [6]. In the social network as we study, besides the topological features and the node attributions, the social circle information, the behaviors of users, the contexts users posting and the neighbors' impacts also give clues of whether a link is exist between the two users.…”

Data Stream Mining Based Dynamic Link Anomaly Analysis Using Paired Sliding Time Window Data

“…For this report, the Jaccard index is selected as a node-dependent metric to characterize nodes' community similarity. It is defined as the size of the intersection divided by the size of the union of the sets [5]. In the graph G, for each pair of nodes v, u ∈ V , the Jaccard index is defined as the ratio between the number of their common neighbors and the number of total neighbors, namely:…”