1,381 research outputs found
Parallel Maximum Clique Algorithms with Applications to Network Analysis and Storage
We propose a fast, parallel maximum clique algorithm for large sparse graphs
that is designed to exploit characteristics of social and information networks.
The method exhibits a roughly linear runtime scaling over real-world networks
ranging from 1000 to 100 million nodes. In a test on a social network with 1.8
billion edges, the algorithm finds the largest clique in about 20 minutes. Our
method employs a branch and bound strategy with novel and aggressive pruning
techniques. For instance, we use the core number of a vertex in combination
with a good heuristic clique finder to efficiently remove the vast majority of
the search space. In addition, we parallelize the exploration of the search
tree. During the search, processes immediately communicate changes to upper and
lower bounds on the size of maximum clique, which occasionally results in a
super-linear speedup because vertices with large search spaces can be pruned by
other processes. We apply the algorithm to two problems: to compute temporal
strong components and to compress graphs.Comment: 11 page
Kernel Graph Convolutional Neural Networks
Graph kernels have been successfully applied to many graph classification
problems. Typically, a kernel is first designed, and then an SVM classifier is
trained based on the features defined implicitly by this kernel. This two-stage
approach decouples data representation from learning, which is suboptimal. On
the other hand, Convolutional Neural Networks (CNNs) have the capability to
learn their own features directly from the raw data during training.
Unfortunately, they cannot handle irregular data such as graphs. We address
this challenge by using graph kernels to embed meaningful local neighborhoods
of the graphs in a continuous vector space. A set of filters is then convolved
with these patches, pooled, and the output is then passed to a feedforward
network. With limited parameter tuning, our approach outperforms strong
baselines on 7 out of 10 benchmark datasets.Comment: Accepted at ICANN '1
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