Statistical Network Similarity

Article Mechanics

Resistance distance distribution in large sparse random graphs

Pawat Akara-pipattana et al.

Summary: In this paper, we study the distribution of resistance distances in an Erdos-Renyi random graph and develop an auxiliary field representation for this quantity. Using a 1/c expansion and a saddle point evaluation, we analyze the distribution and find that it exhibits different characteristics at different values of c, including the presence of subleading peaks. A more refined saddle point scheme allows us to analytically recover some of these subleading peaks.

JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2022)

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Article Multidisciplinary Sciences

Quantification of network structural dissimilarities based on network embedding

Zhipeng Wang et al.

Summary: This study proposes an efficient network comparison method based on network embedding, which can better quantify the structural dissimilarities between networks and capture some properties of the networks.

ISCIENCE (2022)

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Article Mathematics, Interdisciplinary Applications

An empirical comparison of connectivity-based distances on a graph and their computational scalability

Pierre Miasnikof et al.

Summary: This study compares different distance measures in capturing vertex community structure and their scalability in computation. The findings indicate that amplified commute, Otsuka-Ochiai, and Jaccard distances have an inverse relationship with intra-cluster density, while the computation of amplified commute distance does not scale well to large graphs compared to the other two distances.

JOURNAL OF COMPLEX NETWORKS (2021)

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Article Multidisciplinary Sciences

Overlapping community detection in networks based on link partitioning and partitioning around medoids

Alexander Ponomarenko et al.

Summary: The LPAM method introduces a new approach for detecting overlapping communities in networks, considering different distance functions and evaluating its performance on real life instances and synthetic network benchmarks. It utilizes link partitioning and partitioning around medoids to detect overlapping communities in graphs.

PLOS ONE (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Graph Similarity Description: How Are These Graphs Similar?

Corinna Coupette et al.

Summary: This paper discusses the differences in social networks across different platforms and how information networks change over time. The authors suggest treating graph similarity assessment as a description problem rather than a measurement problem, using the Minimum Description Length principle to capture similarities and differences between input graphs. They propose the MOMO model selection task to find good models and demonstrate its efficacy through experiments on synthetic and real-world graphs.

KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2021)

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Proceedings Paper Computer Science, Hardware & Architecture

Anomaly Detection of Network Streams via Dense Subgraph Discovery

Hao Yan et al.

Summary: Cyber security is considered a significant technical challenge in current times, with the need to detect anomalous patterns in network streams quickly. Previous methods using statistical or machine learning approaches often lack efficiency and precision. The proposed graph-based framework, known as SAD, demonstrates higher accuracy and faster performance in detecting anomalous patterns compared to state-of-the-art algorithms.

30TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS AND NETWORKS (ICCCN 2021) (2021)

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Article Computer Science, Theory & Methods