web communities prasanna desikan (06/13/2002). 2 definition web community: groups of individuals who...

Web Communities

Prasanna Desikan(06/13/2002)

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Definition

Web community: Groups of individuals who share

common interests, together with the web pages most popular among them.

Web page collections with a shared topic.

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Types of Communities Explicitly- defined.

Communities that manifest themselves as newsgroups or as resource collections on directories such as Yahoo!

Implicitly- defined. Communities that result from nature of

content-creation of the web.

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Terms and Definitions

Directed Bipartite Graph: A graph whose nodes set can be partitioned into two sets F and C, and every directed edge in the graph is from a node u in F to a node v in C.

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Terms and Definitions

Completed Bipartite Graph: A bipartite graph that contains all possible edges between a vertex of F and a vertex of C.

Core: A complete bipartite sub-graph with at least i nodes from F and at least j nodes from C. In the web world, the i pages the contains the

links are referred to as ‘fans’ and the j pages that are referenced as ‘centers’.

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Inferring Web Communities From Link Topology

Community is a core of central authoritative pages linked together by hub pages.

Identify communities corresponding to the principal and non-principal eigenvectors discovered by HITS.

For communities on broad topics: the grouping of pages discovered is relatively independent of the exact choice of root set.

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Inferring Web Communities From Link Topology

Findings on Structure of Communities. Robustness: For broad topics, HITS

produces stable, robust communities. Topic Generalization: HITS tend to

generalize topics that are not broad. “Michael Jordan” produces links to pages on

MJ and his team. “Dennis Ritchie” produces links that reference

to “C – Programming Language.”

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Inferring Web Communities From Link Topology

Other Generalization: HITS tends to converge on topics with greater density of linkage. E.g for a query on “linguistics”, the top authorities are

focused on a sub-topic “computational linguistics” because of its greater density of linkage on web.

Temporal Issues: For obtaining long-term “core” of a topic, we can superimpose the results of HITS on the same topic, spaced-out several month periods.

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Trawling the Web for Emerging Web Communities

Trawling: Systematic Enumeration of emerging communities from web crawl.

Scan through a web crawl and identify all instances of graph structures that are indicative signatures of communities.

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Trawling the Web for Emerging Web Communities

Data Source: A copy of web from Alexa.Pre-processing data. Identify potential fan pages (a page that

has links to at least six different websites) – out of 200 million pages around 24 million were extracted.

Eliminate mirrors (out of 24 million it removed around 60% of pages.

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Trawling the Web for Emerging Web Communities

Prune by in-degree. Eliminate all pages that have an in-degree

greater than a threshold value k. k is set as 50 in the experiments.

Iterative pruning. When looking for (i,j) cores any potential fan

with out-degree smaller than j can be pruned and the corresponding edges deleted from the graph.

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Trawling the Web for Emerging Web Communities

Inclusion-exclusion pruning. Let {c1,c2,…..,cj} be centers adjacent to

a fan x. N(ct) = neighborhood of ct, the set of

fans that point to ct. x is a part of core if and only if the

intersection of sets N(ct) has size at least i.

Filter nepotistic cores.

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Trawling the Web for Emerging Web Communities

Evaluation of Communities. Fossilization: 30% of communities were

fossilized. A fossil is a community core not all of whose

fans exist on the web today. Reliability: Only 4% of the trawled cores

were coincidental i.e a collection of fan pages without any cogent theme unifying them.

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Trawling the Web for Emerging Web Communities

Quality: 56% were not in Yahoo as constructed from the crawl. And 29% were not in Yahoo at the time of the paper. This indicates identification of emerging

communities by trawling.

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Self Organization and Identification of Web Communities

Web community is defined as a collection of web pages such that each member page has more hyperlinks (in either direction) within the community than outside of the community.

Approach: Maximal Flow – Minimal Cut framework.

Benefits: Focused crawling, automatic population of portal categories.

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A Simple Community Identification Example

Figure : Maximum Flow methods will separate the two subgraphs with any choice of s and t that has s on the left subgraph and t on the right subgraph, removing the three dashed links.

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Approximate Flow Community

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Exact Flow Community

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Exact Flow Community

An artificial source ‘s’, is added with infinite capacity edges routed to all seed vertices in S.

Each pre-existing edge is made bi-directional and rescaled to a constant value k.

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Exact Flow Community

All vertices except the source, sink, and seed vertices are routed to the artificial sink with unit capacity.

A residual flow graph is produced by a maximum flow procedure.

All vertices accessible from s through non-zero positive edges form the desired result and satisfy our definition of a community.

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Sample Results From Community Identification

The scores are the total number of inbound and outbound links that a web page has to other pages that are also in the community.

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Characterization of Communities

Table 3: The fifteen most significant text features for each community, sorted in descending order of the Kullback-Leibler metric.

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Discovering Seeds of New Interest Spread From Premature Pages.

A method for discovering topics, which stimulate communities of people into earnest communications on the topics’ meaning, and grow into a trend of popular interest.

Community is a group of people sharing some value.

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Agora Method on Links Archive page - Page of highest rank

according to Google in a community.

Agora Pages - Pages linked from multiple archive-pages but are not in any community themselves are taken as novel topics attracting multiple communities, called agora-topic pages.

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Agora Method on Links Step 1: A query representing user’s

interest domain is entered to a search engine (Google here, obtaining 105 to 106 pages).

Step 2: Communities, of pages obtained in Step 1, are obtained and archive-pages are selected from communities.

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Agora Method on Links Step 3: Pages, not in the

communities but linked from multiple archive-pages, are obtained as agora-pages. Having all obtained results by here, archive pages (black nodes), agora-pages (red nodes) and the links between them are visualized as in Fig.1.

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Fig: The output of Agora on Links, for domain query “Human Genome”

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Evaluation Stage 1. An interest domain is fixed, a group

of people relevant to the domain gathered, and the domain-name is input as a query (e.g. ”information retrieval”).

Stage 2. The output graph adding real and fake red nodes, as if they all were really obtained as agora-pages, is shown to the subjects. That is, some red nodes, not really obtained, were added with red links to black archive-nodes. Subjects reported individual impressions and exchanged ideas in the group.

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Sample Results Institutes in ‘red’ were the ones who

have data sources of human or mouse genomes, and is useful for researchers in other institutes to look at those data.

8 of the 12 ‘red’ nodes were termed as “interesting for thinking of future work” by the subjects.

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References [1]D. Gibson, J. Klienberg, and P.Raghavan. Inferring web

communties from link topology. In Proc. 9th ACM Conference on Hypertext and Hypermedia.

[2]Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, and Andrew Tomkins. Trawling the web for emerging cyber-communities. In Proc 8th Int. World Wide Web Conf.,1999.

[3] Gary William Flake, Steve Lawrence, C. Lee Giles . Efficient Identification of Web Communities. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

[4] Gary William Flake, Steve Lawrence, C. Lee Giles, Frans M. Coetzee. Self-Organization and Identification of Web Communities. IEEE Computer, 35(3), 66–71, 2002.

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References [5] Naohiro Matsumura , Yukio Ohsawa , Mitsuru Ishizuka

Discovering Seeds of New Interest Spread from Premature Pages Cited by Multiple Communities, 2001 International Conference on Web Intelligence.

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Kullback-Leibler Metric

Let p and q be probability distributions with support X and Y respectively. The relative entropy or Kullback-Liebler distance between two probability distributions p and q is defined as

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