By Evimaria Terzi, Marco Winkler (auth.), Alan Frieze, Paul Horn, Paweł Prałat (eds.)

This e-book constitutes the refereed lawsuits of the eighth foreign Workshop on Algorithms and versions for the Web-Graph, WAW 2011, held in Atlanta, GA, in may possibly 2011 - co-located with RSA 2011, the fifteenth foreign convention on Random constructions and Algorithms.
The thirteen revised complete papers offered including 1 invited lecture have been conscientiously reviewed and chosen from 19 submissions. Addressing a large choice of themes on the topic of the learn of the Web-graph equivalent to theoretical and empirical research, the papers function unique examine by way of algorithmic and mathematical research in all parts touching on the World-Wide net with detailed concentration to the view of complicated information as networks.

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Extra info for Algorithms and Models for the Web Graph: 8th International Workshop, WAW 2011, Atlanta, GA, USA, May 27-29, 2011. Proceedings

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See [6] for detailed experimental results. 2 Random Graphs To demonstrate that the structure we have found in social graphs is not merely a random artifact, a similar set of experiments is carried out for random graphs. Random graph models do not produce clusters as social graphs do. The cores obtained by the (α, β)-Community algorithm usually have significant overlap among them, and correspond to dense regions due to the way the graph was generated. This contrast between social graphs and random graphs again verifies the existence of community structure in various large-scale social networks.

We study clustering on graphs with multiple edge types. Our main motivation is that similarities between objects can be measured in many different metrics, and so allowing graphs with multivariate edges significantly increases modeling power. In this context the clustering problem becomes more challenging. Each edge/metric provides only partial information about the data; recovering full information requires aggregation of all the similarity metrics. We generalize the concept of clustering in single-edge graphs to multi-edged graphs and discuss how this generates a space of clusterings.

In this example each point on the plane represents a vertex, and two vertices are connected by an edge if they are close in distance. The similarity/weight for each edge is inversely proportional to the Euclidean distance. We see visually that there are nine natural clusters. More interestingly we see that these clusters are arranged symmetrically along two axes. , 9}. Instead they have the structure {1, 2, 3} × {1, 2, 3}. An example of such a structure would be the separation of academic papers along two factors, {Physics, Mathematics, Biology} and {West Coast, Midwest, East Coast}.

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