Titlebook: Advances in Knowledge Discovery and Data Mining; 17th Pacific-Asia Co Jian Pei,Vincent S. Tseng,Guandong Xu Conference proceedings 2013 Spr

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Steven Dymarkowski MD, PhD,Hilde Bosmans PhDdom and scale-free graphs, there may be a great variation in the performances of indexes of graph queries. Unfortunately, the structures of graph indexes are often complex and ., so deriving an accurate performance model is a daunting task. As a result, database practitioners may encounter difficult

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Nonischemic Myocardial Disease,ry that the relative frequencies obey a power-law (sub-linear, or super-linear), for a wide variety of diverse settings: tasks in a phone-call network, like count of friends, count of phone-calls, total count of minutes; tasks in a twitter-like network, like count of tweets, count of followees etc.

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Hubert F. Baars,Jeroen F. van der Heijdenicant research interest. The aim of graph stream classification is to learn a discriminative model from a stream of graphs represented by sets of edges on a complex network. In this paper, we propose a fast graph stream classification method using DIscriminative Clique Hashing (DICH). The main idea