11 papers · ranked by Valyu relevance
Anders Høst-Madsen, Jun Zhang
—This paper has dual aims. First is to develop practical universal coding methods for unlabeled graphs. Second is to use these for graph anomaly detection. The paper develops two coding methods for unlabeled graphs: one based on the degree distribution, the second based on the triangle distribution. It is shown that…
Muhammad Umair, Young-Koo Lee
Graph data are pervasive worldwide, e.g., social networks, citation networks, and web graphs. A real-world graph can be huge and requires heavy computational and storage resources for processing. Various graph compression techniques have been presented to accelerate the processing time and utilize memory efficiently.…
Hung T. Nguyen, Pierre Jinghong Liang, Leman Akoglu
Within a large database G containing graphs with labeled nodes and directed, multi-edges; how can we detect the anomalous graphs? Most existing work are designed for plain (unlabeled) and/or simple (unweighted) graphs. We introduce CODEtect, the first approach that addresses the anomaly detection task for graph…
Mojtaba Abolfazli, Anders Høst-Madsen, Jun Zhang, András Bratincsák
—Many multivariate data such as social and biological data exhibit complex dependencies that are best characterized by graphs. Unlike sequential data, graphs are, in general, unordered structures. This means we can no longer use classic, sequential-based compression methods on these graph-based data. Therefore, it is…
Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael M. Bronstein
'Michael M. Bronstein'] Can we use machine learning to compress graph data? The absence of ordering in graphs poses a significant challenge to conventional compression algorithms, limiting their attainable gains as well as their ability to discover relevant patterns. On the other hand, most graph compression approaches…
Prathyush Poduval, Haleh Alimohamadi, Ali Zakeri, Farhad Imani + 3 more
'M. Hassan Najafi' 'Tony Givargis' 'Mohsen Imani'] Memorization is an essential functionality that enables today's machine learning algorithms to provide a high quality of learning and reasoning for each prediction. Memorization gives algorithms prior knowledge to keep the context and define confidence for their…
Luca Cappelletti, Tommaso Fontana, Elena Casiraghi, Vida Ravanmehr + 7 more
'Tiffany J. Callahan' 'Carlos Cano' 'Marcin P. Joachimiak' 'Christopher J. Mungall' 'Peter N. Robinson' 'Justin Reese' 'Giorgio Valentini'] Graph representation learning methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise…
Alexis Bénichou, Jean-Baptiste Masson, Christian L. Vestergaard, Fabrizio De Vico Fallani
Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement…
Lloyd Allison
TR #2014/2771 . This report concerns the information content of a graph, optionally conditional on one or more background, "common knowledge" graphs. It describes an algorithm to estimate this information content, and includes some examples based on chemical compounds. keywords: Graph, network, complexity, information…
Van Thuy Hoang, Hyeon-Ju Jeon, Eun-Soon You, Yoewon Yoon + 3 more
Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional…
Xueyuan Chen, Shangzhe Li, Yanchun Liang
Due to the success observed in deep neural networks with contrastive learning, there has been a notable surge in research interest in graph contrastive learning, primarily attributed to its superior performance in graphs with limited labeled data. Within contrastive learning, the selection of a “view” dictates the…