Titlebook: Community Search over Big Graphs; Xin Huang,Laks V. S. Lakshmanan,Jianliang Xu Book 2019 Springer Nature Switzerland AG 2019

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書目名稱Community Search over Big Graphs影響因子(影響力)




書目名稱Community Search over Big Graphs影響因子(影響力)學(xué)科排名




書目名稱Community Search over Big Graphs網(wǎng)絡(luò)公開度




書目名稱Community Search over Big Graphs網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Community Search over Big Graphs被引頻次




書目名稱Community Search over Big Graphs被引頻次學(xué)科排名




書目名稱Community Search over Big Graphs年度引用




書目名稱Community Search over Big Graphs年度引用學(xué)科排名




書目名稱Community Search over Big Graphs讀者反饋




書目名稱Community Search over Big Graphs讀者反饋學(xué)科排名

和諧

廢止

cauda-equina

Interlude: Intercultural Remixes,th certain relationships. Massive networks can often be understood and analyzed in terms of these communities [165]. In the literature, several different models for cohesive (dense) subgraphs and communities have been proposed, which we will discuss in Chapters 3–6. Specifically, dense subgraphs of

PON

生銹

Birmingham’s Postindustrial Metalch on a simple graph aims to find densely connected communities containing all query nodes. In applications such as analysis of protein protein interaction (PPI) networks, citation graphs, and collaboration networks, nodes tend to have attributes. Most simple structural community search algorithms i

生銹

Brett Lashua,Stephen Wagg,Karl Spracklenwork induced only by his or her friends is called an ego-network. Online social networks allow users to manually categorize their friends into different social circles within their ego-networks (e.g., “circles” on Google+) [117, 170]. The task of social circle discovery is to automatically identify

hegemony

征服

倔強(qiáng)不能

Brett Lashua,Stephen Wagg,M. Selim YavuzThis chapter first lists the community search models that are not detailed in the previous chapters. We then conclude the book by discussing future directions and open problems for further research in community search over large graphs.