%0 Conference Paper %1 citeulike:542510 %A Chakrabarti, Soumen %A Joshi, Mukul M. %A Punera, Kunal %A Pennock, David M. %B WWW '02: Proceedings of the 11th international conference on World Wide Web %C New York, NY, USA %D 2002 %I ACM Press %K community %P 251--262 %R 10.1145/511446.511480 %T The structure of broad topics on the web %U http://dx.doi.org/10.1145/511446.511480 %@ 1581134495

@inproceedings{citeulike:542510, added-at = {2006-09-25T12:54:00.000+0200}, address = {New York, NY, USA}, author = {Chakrabarti, Soumen and Joshi, Mukul M. and Punera, Kunal and Pennock, David M.}, biburl = {https://www.bibsonomy.org/bibtex/242e804cc0f6553146387a981983e8afc/grahl}, booktitle = {WWW '02: Proceedings of the 11th international conference on World Wide Web}, citeulike-article-id = {542510}, comment = {Related work for the Semantic Network Analysis Workshop? --- Use (coarse grained) topic taxonomy DMOZ to train a topic based webpage categorizer. With a crawler, they measure the background distribution of broad topics in the web. They define several random walkers to measure how different pages (Random, Sibling, SameDomain, DiffDomain) link to the same topic categories. They state, that this is something different than studying the decay in TFIDF with the length of the walk (Menczer [25]) For one of the properties they study (Link-based vs. content-based Web communities) they define a "topic citation matrix in which entry (i; j) represents the probability that a page about topic i cites a page about topic j (x5). [...] Typically, if the topics are chosen well and if there is topical clustering on theWeb, we expect to see heavy diagonals in the matrix and small off-diagonal probabilities." --- I also liked their style on defining a probability distribution "Each document d has a class probability vector \vec{p(d)} = (Pr)c|d)\forall c), where \sum_n Pr(c|d)=1." They call it "soft counting" "This is a form of soft-counting. The 'hard' analog would be to assign each d to its highest scoring class and count up the number of document assigned to each class Lewis [22] notes that soft counting gives better estimates than hard counting for small sample sizes" They also state something interesting about the jensen-shannon-divergence: "We had to avoid the use of the more well-known Kullback-Leibler (KL) divergence because of two problems: it is asymmetric, and more seriously, it cannot deal with zero probabilities gracefully. The symmetric Jensen-Shannon (JS) divergence [12] also has problems with zeroes" --- Power law definition: "power law distribution [1, 7, 21]: the proibability that a randomly picked node has degree i is proportional to 1/i^x, for some constant 'power' x > 1" --- papers that cite ""The structure of broad topics on the web" + + A Web Surfer Model Incorporating Topic Continuity - group of 3 » BL Narayan, S Dutta - IEEE Transactions on Knowledge and Data Engineering, 2005 - doi.ieeecomputersociety.org This paper describes a surfer model which incorporates information about topic continuity derived from the surfer's history. Therefore, unlike earlier models, ... Cited by 1 - Web Search + + Mapping the Semantics of Web Text and Links - group of 4 » F Menczer - IEEE Internet Computing, 2005 - doi.ieeecomputersociety.org Search: Advanced Search. ... 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Web Search}, doi = {10.1145/511446.511480}, interhash = {bbeafbf042320a27ec0a5fae7ba5b3b3}, intrahash = {42e804cc0f6553146387a981983e8afc}, isbn = {1581134495}, keywords = {community}, pages = {251--262}, priority = {0}, publisher = {ACM Press}, timestamp = {2006-09-25T12:54:00.000+0200}, title = {The structure of broad topics on the web}, url = {http://dx.doi.org/10.1145/511446.511480}, year = 2002 }