Fixed-memory and Uncertainty Reducing Local Triangle Counting for Multigraph Streams


Given a multigraph stream (e.g., Facebook messages or network traffic) where duplicate edges arrive continuously, how can we accurately and memory-efficiently estimate local triangles for all nodes? Local triangle counting in a graph stream is one of the fundamental tasks in graph mining with important applications including anomaly detection, social role identification, community detection, etc. Many recent graph streams include duplicate edges, hence form multigraph streams: e.g., many network packets might have a same (source, destination) pair. Although there have been several local triangle counting methods for multigraph streams, they have problems in terms of accuracy and memory efficiency; furthermore, most methods support either binary or weighted counting, and thus cannot find anomalies whose detection requires both types of counting. In this paper, we propose FURL, a memory-efficient and accurate local triangle counting method for multigraph streams. FURL has two main advantages. First, FURL improves accuracy by 1) reducing the variance of its estimation via a regularization strategy, and 2) sampling more triangles than the state-of-the-art methods do, by efficiently using memory space. Second, FURL finds anomalies which state-of-the-art methods cannot discover. Experimental results show that FURL outperforms state-of-the-art methods in terms of accuracy and memory efficiency. Thanks to FURL, we discover interesting anomalies from a Bitcoin network.


Here is a download link for our FURL implementation. [Download]


FURL is described in the following paper.


Name # Nodes # Edges Description Name # Nodes # Edges Description
Facebook 45,813 855,542 Wall posts on other user's wall on Facebook DBLP 1,314,050 18,986,618 Co-author network in DBLP
Enron 86,978 1,134,990 Enron email network Bitcoin 6,297,539 28,143,065 Bitcoin transaction network
WikiGER 506,174 4,555,759 Communication network of the German Wikipedia Livejournal 4,847,571 68,475,391 Social network of LiveJournal