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» Graph Mining using Graph Pattern Profiles
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KDD
2008
ACM
193views Data Mining» more  KDD 2008»
16 years 2 months ago
A family of dissimilarity measures between nodes generalizing both the shortest-path and the commute-time distances
This work introduces a new family of link-based dissimilarity measures between nodes of a weighted directed graph. This measure, called the randomized shortest-path (RSP) dissimil...
Luh Yen, Marco Saerens, Amin Mantrach, Masashi Shi...
SDM
2009
SIAM
149views Data Mining» more  SDM 2009»
15 years 11 months ago
Near-optimal Supervised Feature Selection among Frequent Subgraphs.
Graph classification is an increasingly important step in numerous application domains, such as function prediction of molecules and proteins, computerised scene analysis, and an...
Alexander J. Smola, Arthur Gretton, Hans-Peter Kri...
129
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DSN
2007
IEEE
15 years 6 months ago
A Real-Time Network Traffic Profiling System
This paper presents the design and implementation of a real-time behavior profiling system for high-speed Internet links. The profiling system uses flow-level information from con...
Kuai Xu, Feng Wang 0002, Supratik Bhattacharyya, Z...
PAKDD
2007
ACM
224views Data Mining» more  PAKDD 2007»
15 years 8 months ago
Graph Nodes Clustering Based on the Commute-Time Kernel
This work presents a kernel method for clustering the nodes of a weighted, undirected, graph. The algorithm is based on a two-step procedure. First, the sigmoid commute-time kernel...
Luh Yen, François Fouss, Christine Decaeste...
PAKDD
2010
ACM
169views Data Mining» more  PAKDD 2010»
15 years 7 months ago
oddball: Spotting Anomalies in Weighted Graphs
Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the OddBall algorithm, to find such nod...
Leman Akoglu, Mary McGlohon, Christos Faloutsos