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150 7 Extracting and Analyzing Web Social NetworksTable 7.2. umber of main lines in split or merged communitiestk 1 : Aug. 99 Aug. 00 Oct. 01tk: Aug. 00 Oct. 01 Feb. 02# Branch lines at 28,467 32,490 41,501tk 1# Main lines 26,723 34,396 83,771# Branch lines at tk 29,722 41,752 44,305Types of ChangesAlthough the size distribution of communities is stable, the structure of communities changesdynamically. Figure 6 shows how many communities are involved in each type of changesfrom t k1 to t k . Each bar represents the number of communities in charts at the time. Barsat 2000 and 2001 are split vertically, since they have the previous and the next charts to becompared. Each block represents the number of communities involved in a particular change.Dotted blocks represent dissolved communities from t k1 , and white blocks represent emergedcommunities. Gray blocks represent communities that are involved in split or merge. Finally,black blocks represent single communities that are not involved in these changes, but maygrow or shrink.The structure of the chart changes mainly by split and merge, in which more than half ofcommunities are involved. The number of single communities is small (10% in 1999, 14%in 2000, and 25% in 2001). Since the seed sets of the charts are stable parts in our archives,the number of communities dissolved from each chart is rather small. About 24% to 30% ofcommunities are dissolved in one year from 1999 to 2001, while 20% of communities aredissolved in four months from 2001.Changes by split and merge are complicated, since split communities may be mergedwith other communities in the next time. However, it is not totally chaotic. Rather stablecommunities by extracting main lines are seen. Table 7.2 shows the number of main lines andbranch lines in each intervals. About half of survived (not dissolved) communities in 1999and 2000 are included in main lines for one year, and about 66% of survived communitiesin 2001 are included in main lines for four months. Note that the main lines include singlecommunities. Those main lines can be used as a good starting point for finding changes aroundthe topic. In the following section, we show the detailed behavior of each changes.Split and Merged CommunitiesThe distributions of the split rate and merge rate are fist shown in Figure 7.2 and 7.3.They plot the number of split or merged communities as a function of the number of splitand merged URLs (i.e., Nsp and Nmg) in the log-log scale. Both distributions roughly followthe power law, and show that split or merge rate is small in most cases. Their shapes andscales are also similar. That is, when communities at t k1 split with a split rate, almost the samenumber of communities are merged at t k with the same rate as the split rate. This symmetry ispart of the reason why the size distribution of communities does not change so much.
7.1 Extracting Evolution of Web Community from a Series of Web Archive 151Fig. 7.2. Distribution of split rate.Fig. 7.3. Distribution of split rate.Emerged and Dissolved CommunitiesThe size distributions of emerged and dissolved communities also follow the power law, andcontribute to preserve the size distribution of communities. Figure 7.4 and 7.5 show thesedistributions for all periods in the log-log scale.In most cases, the exponent of the power law is greater than 3.2, while the exponent of thewhole chart is around 3.0. This means that small communities are easy to emerge and dissolve.Growth RateFinally, the distribution of the growth rate is examined. Figure 7.6 shows the number of communitiesas a function of the growth rate.
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Web Mining and Social Networking
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Guandong Xu • Yanchun Zhang • L
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VIIIPrefacefollowing characteristic
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Acknowledgements: We would like to
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XIVContents3.1.2 Basic Algorithms f
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XVIContentsPart III Social Networki
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Part IFoundation
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4 1 Introduction(3). Learning usefu
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6 1 Introductioncalled computationa
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8 1 Introduction• The data on the
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10 1 Introductionin a broad range t
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2Theoretical BackgroundsAs discusse
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2.2 Textual, Linkage and Usage Expr
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2.4 Eigenvector, Principal Eigenvec
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2.5 Singular Value Decomposition (S
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2.6 Tensor Expression and Decomposi
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2.7 Information Retrieval Performan
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2.8 Basic Concepts in Social Networ
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2.8 Basic Concepts in Social Networ
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30 3 Algorithms and TechniquesTable
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68 3 Algorithms and Techniquesthat
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4Web Content MiningIn recent years
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score(q,d)=4.2 Web Search 73V(q) ·
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4.2 Web Search 75algorithm. The Web
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4.3 Feature Enrichment of Short Tex
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4.4 Latent Semantic Indexing 794.4
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Notation4.5 Automatic Topic Extract
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4.5 Automatic Topic Extraction from
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4.6 Opinion Search and Opinion Spam
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4.6 Opinion Search and Opinion Spam
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90 5 Web Linkage Mining5.2 Co-citat
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98 5 Web Linkage MiningNext, consid
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200 References82. C. Dwork, R. Kuma
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204 References151. A. N. Langville
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208 References223. C. Shahabi, A. M
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210 References260. G.-R. Xue, D. Sh
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