Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
Matrix multiplication via arithmetic progressions
Journal of Symbolic Computation - Special issue on computational algebraic complexity
Shortest paths algorithms: theory and experimental evaluation
Mathematical Programming: Series A and B
Communications of the ACM
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Distance estimation and object location via rings of neighbors
Proceedings of the twenty-fourth annual ACM symposium on Principles of distributed computing
Meridian: a lightweight network location service without virtual coordinates
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Using structure indices for efficient approximation of network properties
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph clustering with network structure indices
Proceedings of the 24th international conference on Machine learning
All-pairs nearly 2-approximate shortest paths in O(n2polylogn) time
Theoretical Computer Science
A machine learning approach to building domain-specific search engines
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Using complex network features for fast clustering in the web
Proceedings of the 20th international conference companion on World wide web
Quantifying sentiment and influence in blogspaces
Proceedings of the First Workshop on Social Media Analytics
Hi-index | 0.00 |
As a typical social media in Web 2.0, blogs have attracted a surge of researches. Unlike the traditional studies, the social networks mined from Internet are very large, which makes a lot of social network analyzing algorithms to be intractable. According to this phenomenon, this paper addresses the novel problem of efficient social networks analyzing on blogs. This paper turns to account the structural characteristics of real large-scale complex networks, and proposes a novel shortest path approximate algorithm to calculate the distance and shortest path between nodes efficiently. The approximate algorithm then is incorporated with social network analysis algorithms and measurements for large-scale social networks analysis. We illustrate the advantages of the approximate analysis through the centrality measurements and community mining algorithms. The experiments demonstrate the effectiveness of the proposed algorithms on blogs, which indicates the necessity of taking account of the structural characteristics of complex networks when optimizing the analysis algorithms on large-scale social networks.