C4.5: programs for machine learning
C4.5: programs for machine learning
On inferring autonomous system relationships in the internet
IEEE/ACM Transactions on Networking (TON)
Machine Learning
Modeling interactome: scale-free or geometric?
Bioinformatics
DIMES: let the internet measure itself
ACM SIGCOMM Computer Communication Review
FANMOD: a tool for fast network motif detection
Bioinformatics
Efficient Detection of Network Motifs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Efficient semi-streaming algorithms for local triangle counting in massive graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Main-memory triangle computations for very large (sparse (power-law)) graphs
Theoretical Computer Science
Ten years in the evolution of the internet ecosystem
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Approximating the Number of Network Motifs
WAW '09 Proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph
Structure of Neighborhoods in a Large Social Network
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Speeding up logistic model tree induction
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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Counting network graphlets (and motifs) was shown to have an important role in studying a wide range of complex networks. However, when the network size is large, as in the case of the Internet topology and WWW graphs, counting the number of graphlets becomes prohibitive for graphlets of size 4 and above. Devising efficient graphlet counting algorithms thus becomes an important goal. In this paper, we present efficient counting algorithms for 4-node graphlets. We show how to efficiently count the total number of each type of graphlet, and the number of graphlets adjacent to a node. We further present a new algorithm for node position-aware graphlet counting, namely partitioning the graphlet count by the node position in the graphlet. Since our algorithms are based on non-induced graphlet count, we also show how to calculate the count of induced graphlets given the non-induced count. We implemented our algorithms on a set of both synthetic and real-world graphs. Our evaluation shows that the algorithms are scalable and perform up to 30 times faster than the state-of-the-art. We then apply the algorithms on the Internet Autonomous Systems (AS) graph, and show how fast graphlet counting can be leveraged for efficient and scalable classification of the ASes that comprise the Internet. Finally, we present RAGE, a tool for rapid graphlet enumeration available online.