The Link Prediction Problem for Social Networks - Arts & Humanities Assignment Help

Assignment Task

Abstract
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 for analyzing 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.

Introduction
As part of the recent surge of research on large, complex networks and their properties, a considerable amount of attention has been devoted to the computational analysis of social networks| structures whose nodes represent people or other entities embedded in a social context, and whose edges represent interaction, collaboration, or in uence between entities. Natural examples of social networks include the set of all scientists in a particular discipline, with edges joining pairs who have co-authored papers; the set of all employees in a large company, with edges joining pairs working on a common project; or a collection of business leaders, with edges joining pairs who have served together on a corporate board of directors. The availability of large, detailed datasets encoding such networks has stimulated extensive study of their basic properties, and the identification of recurring structural features. (See, for example, the work of Watts and Strogatz [28], Watts [27], Grossman [9], Newman [19], and Adamic and Adar [1], or, for a thorough recent survey, Newman [20].)

Social networks are highly dynamic objects; they grow and change quickly over time through the addition of new edges, signifying the appearance of new interactions in the underlying social structure. Understanding the mechanisms by which they evolve is a fundamental question that is still not well understood, and it forms the motivation for our work here. We define and study a basic computational problem underlying social network evolution, the link prediction problem: 

Given a snapshot of a social network at time t, we seek to accurately predict the edges that will be added to the network during the interval from time t to a given future time t0.

In effect, the link prediction problem asks: to what extent can the evolution of a social network be modeled using features intrinsic to the network itself? Consider a co-authorship network among scientists, for example. There are many reasons, exogenous to the network, why two scientists who have never written a paper together will do so in the next few years: for example, they may happen to become geographically close when one of them changes institutions. Such collaborations can be hard to predict.

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