10 papers with code ·
Graphs

Graph similarity/distance computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, but very costly to compute in practice.

Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements.

CVPR 2019 • liqimai/Efficient-SSL •

However, existing graph-based methods either are limited in their ability to jointly model graph structures and data features, such as the classical label propagation methods, or require a considerable amount of labeled data for training and validation due to high model complexity, such as the recent neural-network-based methods.

WSDM '19 Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining 2019 • yunshengb/SimGNN •

Our model achieves better generalization on unseen graphs, and in the worst case runs in quadratic time with respect to the number of nodes in two graphs.

SOTA for Graph Similarity on IMDb

cnmusco/graph-similarity-learning

•In this work we consider a privacy threat to a social network in which an attacker has access to a subset of random walk-based node similarities, such as effective resistances (i. e., commute times) or personalized PageRank scores.

giannisnik/message_passing_graph_kernels

•The first component is a kernel between vertices, while the second component is a kernel between graphs.

The von Neumann graph entropy (VNGE) facilitates measurement of information divergence and distance between graphs in a graph sequence.

IBM/SpectralClustering_RandomBinning

•Then we introduce a state-of-the-art SVD solver to effectively compute eigenvectors of this large matrix for spectral clustering.

Finally, we create an ensemble-based classifier using AMF, AMFP, and existing link prediction methods and obtain an area under the receiver operating characteristic curve of 0. 814 and 0. 991 for the retrospective and the holdout analyses.