An Efficient Approach to Finding Dense Temporal Subgraphs

Ma, Shuai; Hu, Ruifeng; Wang, Luoshu; Lin, Xuelian; Huai, Jinpeng

doi:10.1109/tkde.2019.2891604

Cited by 12 publications

(3 citation statements)

References 34 publications

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“…Finding dense subgraphs in dynamic graphs has also received much attention. For example, Ma et al (2019) detected dense temporal subgraphs. Epasto et al (2015) addressed the problem of finding densest subgraphs efficiently in dynamic graphs.…”

Section: Introductionmentioning

confidence: 99%

USNAP: fast unique dense region detection and its application to lung cancer

Wong,

Pastrello,

Kotlyar

et al. 2023

Bioinformatics

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Motivation Many real-world problems can be modeled as annotated graphs. Scalable graph algorithms that extract actionable information from such data are in demand since these graphs are large, varying in topology, and have diverse node/edge annotations. When these graphs change over time they create dynamic graphs, and open the possibility to find patterns across different time points. In this paper, we introduce a scalable algorithm that finds unique dense regions across time points in dynamic graphs. Such algorithms have applications in many different areas, including the biological, financial, and social domains. Results There are three important contributions to this manuscript. First, we designed a scalable algorithm, USNAP, to effectively identify dense subgraphs that are unique to a time stamp given a dynamic graph. Importantly, USNAP provides a lower bound of the density measure in each step of the greedy algorithm. Second, insights and understanding obtained from validating USNAP on real data show its effectiveness. While USNAP is domain independent, we applied it to four non-small cell lung cancer (NSCLC) gene expression datasets. Stages in NSCLC were modeled as dynamic graphs, and input to USNAP. Pathway enrichment analyses and comprehensive interpretations from literature show that USNAP identified biologically relevant mechanisms for different stages of cancer progression. Third, USNAP is scalable, and has a time complexity of O(m + mclognc + nclognc), where m is the number of edges, and n is the number of vertices in the dynamic graph; mc is the number of edges, and nc is the number of vertices in the collapsed graph. Availability The code of USNAP is available at https://www.cs.utoronto.ca/∼juris/data/USNAP22. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

USNAP: fast unique dense region detection and its application to lung cancer

Wong,

Pastrello,

Kotlyar

et al. 2023

Bioinformatics

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“…We model the local correlations among parking lots as a graph G = (V, E, A), where V = P is the set of parking lots, E is a set of edges indicating connectivity among parking lots, and A denotes the proximity matrix of G (Ma et al 2019). Specifically, we define the connectivity constraint e ij ∈ E as…”

Section: Contextual Graph Convolutionmentioning

confidence: 99%

Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction

Zhang

Líu

Liu

et al. 2020

AAAI

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The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting city-wide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of \hmgnn outperforms seven state-of-the-art baselines.

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“…Other works related to ours include Semertzidis et al [19], who introduced the problem of identifying a set of vertices that are densely connected in at least 𝑘 timestamps of a temporal network; Himmel at al. [8] and Viard et al [21], who generalized the concept of clique in a temporal graph and proposed the respective listing algorithms; and Ma et al [14], who a proposed a statistics-driven approach to find dense temporal subgraphs in large temporal networks.…”

Section: Related Workmentioning

confidence: 99%

Efficient Algorithms to Mine Maximal Span-Trusses From Temporal Graphs

Lotito,

Montresor

2020

Preprint

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Over the last decade, there has been an increasing interest in temporal graphs, pushed by a growing availability of temporallyannotated network data coming from social, biological and financial networks.Despite the importance of analyzing complex temporal networks, there is a huge gap between the set of definitions, algorithms and tools available to study large static graphs and the ones available for temporal graphs.An important task in temporal graph analysis is mining dense structures, i.e., identifying high-density subgraphs together with the span in which this high density is observed.In this paper, we introduce the concept of (𝑘, Δ)-truss (spantruss) in temporal graphs, a temporal generalization of the 𝑘-truss, in which 𝑘 captures the information about the density and Δ captures the time span in which this density holds. We then propose novel and efficient algorithms to identify maximal span-trusses, namely the ones not dominated by any other span-truss neither in the order 𝑘 nor in the interval Δ, and evaluate them on a number of public available datasets. CCS CONCEPTS• Theory of computation → Graph algorithms analysis; • Mathematics of computing → Graph theory; • Information systems → Spatial-temporal systems; Data mining; Web searching and information discovery.

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An Efficient Approach to Finding Dense Temporal Subgraphs

Cited by 12 publications

References 34 publications

USNAP: fast unique dense region detection and its application to lung cancer

USNAP: fast unique dense region detection and its application to lung cancer

Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction

Efficient Algorithms to Mine Maximal Span-Trusses From Temporal Graphs

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