**Abstract**

Many social, biological,
and information systems can be well described by network. Where nodes represent
individuals, biological elements (proteins, genes, etc.), computers, web users,
and so on and links denote the relations or interactions between nodes.

A network is represented
by a mathematical structure called a graph. Where a graph G is a structure
consisting of a set of vertices V (also called nodes), and a set of edges E
(also called arcs or links).Great efforts have been made to understand the evolution
of networks (by R. Albert, A.-L. Barab ́asi). Social networks are very dynamic
objects, since new edges and vertices are added to the graph over the time.
Understanding the dynamics that drives the evolution of social network is a
complex problem due to a large number of variables. But, a comparatively easier
problem is to understand the association between two specific nodes. Hence we
predict the likelihood of a future association between two nodes, knowing that
there is no association between the nodes in the current state of the graph.
Predicting certain changes to a social network is called the link prediction
problem. Liben-Nowell & Kleinberg (2003) explain it as:

*Given a snapshot of a social network at time t, we need to accurately predict the edges that will be added to the network during the interval from time t to a given future time t' where t < t' .*

Link prediction has also
many applications outside the domain of social networks. For example, in
e-commerce it can help build recommendation systems; in the security domain
link prediction identifying the structure of a criminal network (i.e.,
predicting missing links in a criminal network using incomplete data); in
bioinformatics link prediction can be used to find interactions between proteins
etc.

**Speaker**

**:**

**Kushal Veer Singh,**

Research
Scholar, SCIS, JNU

**Venue: Committee Room, Central Library, JNU**

**Date and Time:**

**18**

^{th}February 2014 (Tuesday)**, 4:30 pm**