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10 - Mining Social-Network Graphs

Published online by Cambridge University Press:  05 December 2014

Jure Leskovec ,
Anand Rajaraman  and
Jeffrey David Ullman
Jure Leskovec
Affiliation:
Stanford University, California
Anand Rajaraman
Affiliation:
Milliways Laboratories, California
Jeffrey David Ullman
Affiliation:
Stanford University, California
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Summary

There is much information to be gained by analyzing the large-scale data that is derived from social networks. The best-known example of a social network is the “friends” relation found on sites like Facebook. However, as we shall see there are many other sources of data that connect people or other entities.

In this chapter, we shall study techniques for analyzing such networks. An important question about a social network is how to identify “communities;” that is, subsets of the nodes (people or other entities that form the network) with unusually strong connections. Some of the techniques used to identify communities are similar to the clustering algorithms we discussed in Chapter 7. However, communities almost never partition the set of nodes in a network. Rather, communities usually overlap. For example, you may belong to several communities of friends or classmates. The people from one community tend to know each other, but people from two different communities rarely know each other. You would not want to be assigned to only one of the communities, nor would it make sense to cluster all the people from all your communities into one cluster.

Also in this chapter we explore efficient algorithms for discovering other properties of graphs. We look at “simrank,” a way to discover similarities among nodes of a graph. We explore triangle counting as a way to measure the connectedness of a community. We give efficient algorithms for exact and approximate measurement of the neighborhood sizes of nodes in a graph. Finally, we look at efficient algorithms for computing the transitive closure.

Social Networks as Graphs

We begin our discussion of social networks by introducing a graph model. Not every graph is a suitable representation of what we intuitively regard as a social network. We therefore discuss the idea of “locality,” the property of social networks that says nodes and edges of the graph tend to cluster in communities. This section also looks at some of the kinds of social networks that occur in practice.

Type
Chapter
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Mining of Massive Datasets , pp. 325 - 383
Publisher: Cambridge University Press
Print publication year: 2014

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References

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