Truss Decomposition of Probabilistic Graphs

Huang, Xin; Lu, Wei; Lakshmanan, Laks V. S.

doi:10.1145/2882903.2882913

Cited by 100 publications

(56 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, each vertex u is associated with a private graph G u , where vertices of G u are vertices from the public graph G, and G u is only known to u. -Uncertain graph [94,131,104]. In many real applications (e.g., biology), the graph data are often noisy, inexact, and inaccurate, and they can be modeled as uncertain graphs, where each edge is associated with a value denoting its existence probability.…”

Section: Other Types Of Graphsmentioning

confidence: 99%

A survey of community search over big graphs

et al. 2019

Self Cite

View full text Add to dashboard Cite

With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real-time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus it is desirable to have a comprehensive review of these works.In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze

show abstract

Section: Other Types Of Graphsmentioning

confidence: 99%

A survey of community search over big graphs

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…To support the efficient search of k-truss communities, they design a compact index, which can be updated incrementally under a dynamic graph setting. They also extend the k-truss community detection models to probabilistic graphs [22] and attributed graphs [11]. In particular, for probabilistic graphs, to meet the requirements…”

Section: B K-truss Based Community Detectionmentioning

confidence: 99%

Finding Route Hotspots in Large Labeled Networks

Lei

Zhang

Chu

et al. 2021

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

In many advanced network analysis applications, like social networks, e-commerce, and network security, hotspots are generally considered as a group of vertices that are tightly connected owing to the similar characteristics, such as common habits and location proximity. In this paper, we investigate the formation of hotspots from an alternative perspective that considers the routes along the network paths as the auxiliary information, and attempt to find the route hotspots in large labeled networks. A route hotspot is a cohesive subgraph that is covered by a set of routes, and these routes correspond to the same sequential pattern consisting of vertices' labels. To the best of our knowledge, the problem of Finding Route Hotspots in Large Labeled Networks has not been tackled in the literature. However, it is challenging as counting the number of hotspots in a network is #P-hard. Inspired by the observation that the sizes of hotspots decrease with the increasing lengths of patterns, we prove several anti-monotonicity properties of hotspots, and then develop a scalable algorithm called FastRH that can use these properties to effectively prune the patterns that cannot form any hotspots. In addition, to avoid the duplicate computation overhead, we judiciously design an effective index structure called RH-Index for storing the hotspot and pattern information collectively, which also enables incremental updating and efficient query processing. Our experimental results on real-world datasets clearly demonstrate the effectiveness and scalability of our proposed methods.

show abstract

“…Other Dense Subgraphs. Recently, many other dense subgraph models [24], such as k-core [7,47,51,23,22,20,25,26,67,12], k-truss [15,37,69,39,38], k-(r, s) nucleus [60,58,61,59] (a generalization of k-core and k-truss), k-clique [16,34], k-edge connected components [35,36]. and k-plexes [63], have also been explored.…”

Section: Related Workmentioning

confidence: 99%

Efficient algorithms for densest subgraph discovery

Fang

Cheng

et al. 2019

Proc. VLDB Endow.

View full text Add to dashboard Cite

Densest subgraph discovery (DSD) is a fundamental problem in graph mining. It has been studied for decades, and is widely used in various areas, including network science, biological analysis, and graph databases. Given a graph G, DSD aims to find a subgraph D of G with the highest density (e.g., the number of edges over the number of vertices in D). Because DSD is difficult to solve, we propose a new solution paradigm in this paper. Our main observation is that the densest subgraph can be accurately found through a k-core (a kind of dense subgraph of G), with theoretical guarantees. Based on this intuition, we develop efficient exact and approximation solutions for DSD. Moreover, our solutions are able to find the densest subgraphs for a wide range of graph density definitions, including clique-based-and general pattern-based density. We have performed extensive experimental evaluation on both real and synthetic datasets. Our results show that our algorithms are up to four orders of magnitude faster than existing approaches.

show abstract

Truss Decomposition of Probabilistic Graphs

Cited by 100 publications

References 33 publications

A survey of community search over big graphs

A survey of community search over big graphs

Finding Route Hotspots in Large Labeled Networks

Efficient algorithms for densest subgraph discovery

Contact Info

Product

Resources

About