Temporal networks in biology and medicine: a survey on models, algorithms, and tools

Hosseinzadeh, Mohammad Mehdi; Cannataro, Mario; Guzzi, Pietro Hiram; Dondi, Riccardo

doi:10.1007/s13721-022-00406-x

Cited by 11 publications

(4 citation statements)

References 104 publications

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“…However, for a large network calculating betweenness centrality in a temporal graph is computationally very expensive. On the other hand researchers have found that finding the foremost and fastest walk is a P-hard problem [1,11,12].…”

Section: Issues In Implementing Centrality Measures For Temporal Graphsmentioning

confidence: 99%

“…Static temporal graphs represent a snapshot of the graph at a specific time point, while dynamic temporal graphs represent the evolution of the graph over time. Temporal graphs can be represented as G(V, E, T ) where V is the set of vertices, T is the set of time stamp and E, a set of temporal edges, where each temporal edge is a triplet (u, v, t), with u, v ∈ V and t ∈ T [1]. Temporal graphs are particularly useful for modeling dynamic systems, such as social networks, transportation systems, and biological networks.…”

Section: Introductionmentioning

confidence: 99%

“…In the context of biological networks, temporal graphs can be used to track the evolution of gene expression or protein-protein interactions over the time, providing insights into the underlying mechanisms of disease progression. In [1], Hosseinzadeh et al discussed the temporal graphs and their applications in biology and medicine. In [2], Thompson et al highlight the potential for temporal network theory to advance the understanding of brain disorders, such as Alzheimer's disease, schizophrenia, and depression, which are characterized by abnormal functional connectivity patterns.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Stage-Specific Genes in Disease Progression using Transition Centrality on Disease Temporal Graphs

Sahu,

2023

Preprint

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The search for biomarkers of diseases has traditionally involved analyzing disease data as a whole. But this approach has largely ignored the significance of disease progression at different stages. Acknowledging the fact that different genes may be responsible for disease progression in different stages, we have developed a novel approach to analyse the temporal graphs that employs a modified version of betweenness centrality, known as ''transition centrality''. Applying this measure to the stage-wise data of three common diseases, namely Alzheimer's disease, Parkinson's disease, and Human breast cancer, we identified successfully a set of genes that may be key players in disease progression. The findings reveal that stage-specific genes can be identified using transition centrality as the specificity of these genes in a particular stage was validated through an extensive review of the relevant literature. Remarkably, this analysis yielded a number of promising disease-related genes for each of the diseases that have been studied, such as RARA and NOS1 in Alzheimer's disease, LRRK2 in Parkinson's disease, and BRCA1, ACTB, TYMS, and many others in human breast cancer. Our results demonstrate that the transition centrality analysis can be utilized to identify stage-specific genes of diseases.

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Section: Issues In Implementing Centrality Measures For Temporal Graphsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying Stage-Specific Genes in Disease Progression using Transition Centrality on Disease Temporal Graphs

Sahu,

2023

Preprint

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“…Finally, also the last task we consider, community search in temporal networks, finds applications in high-stake domains including medicine and epidemiology [66]. For instance, the algorithms we propose for community search in temporal networks may be valuable in guiding policy makers in the design of measures for preventing the spread of infectious diseases [28,65].…”

Section: Introductionmentioning

confidence: 99%

Span-core Decomposition for Temporal Networks

Galimberti

Ciaperoni

Barrat

et al. 2020

ACM Trans. Knowl. Discov. Data

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When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). In this article, we tackle this task by introducing a notion of temporal core decomposition where each core is associated with two quantities, its coreness, which quantifies how densely it is connected, and its span, which is a temporal interval: we call such cores span-cores . For a temporal network defined on a discrete temporal domain T , the total number of time intervals included in T is quadratic in | T |, so that the total number of span-cores is potentially quadratic in | T | as well. Our first main contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently. Then, we focus on the problem of finding only the maximal span-cores , i.e., span-cores that are not dominated by any other span-core by both their coreness property and their span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly extract the maximal ones without computing all span-cores. Finally, as a third contribution, we introduce the problem of temporal community search , where a set of query vertices is given as input, and the goal is to find a set of densely-connected subgraphs containing the query vertices and covering the whole underlying temporal domain T . We derive a connection between this problem and the problem of finding (maximal) span-cores. Based on this connection, we show how temporal community search can be solved in polynomial-time via dynamic programming, and how the maximal span-cores can be profitably exploited to significantly speed-up the basic algorithm. We provide an extensive experimentation on several real-world temporal networks of widely different origins and characteristics. Our results confirm the efficiency and scalability of the proposed methods. Moreover, we showcase the practical relevance of our techniques in a number of applications on temporal networks, describing face-to-face contacts between individuals in schools. Our experiments highlight the relevance of the notion of (maximal) span-core in analyzing social dynamics, detecting/correcting anomalies in the data, and graph-embedding-based network classification.

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Disentangling the Computational Complexity of Network Untangling

Froese,

Kunz,

Zschoche

2023

Theory Comput Syst

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We study the network untangling problem introduced by Rozenshtein et al. (Data Min. Knowl. Disc. 35(1), 213–247, 2021), which is a variant of Vertex Cover on temporal graphs–graphs whose edge set changes over discrete time steps. They introduce two problem variants. The goal is to select at most k time intervals for each vertex such that all time-edges are covered and (depending on the problem variant) either the maximum interval length or the total sum of interval lengths is minimized. This problem has data mining applications in finding activity timelines that explain the interactions of entities in complex networks. Both variants of the problem are NP-hard. In this paper, we initiate a multivariate complexity analysis involving the following parameters: number of vertices, lifetime of the temporal graph, number of intervals per vertex, and the interval length bound. For both problem versions, we (almost) completely settle the parameterized complexity for all combinations of those four parameters, thereby delineating the border of fixed-parameter tractability.

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Temporal networks in biology and medicine: a survey on models, algorithms, and tools

Cited by 11 publications

References 104 publications

Identifying Stage-Specific Genes in Disease Progression using Transition Centrality on Disease Temporal Graphs

Identifying Stage-Specific Genes in Disease Progression using Transition Centrality on Disease Temporal Graphs

Span-core Decomposition for Temporal Networks

Disentangling the Computational Complexity of Network Untangling

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