COVID-19 and Networks

New Generation Computing ◽

10.1007/s00354-021-00134-2 ◽

2021 ◽

Author(s):

Tsuyoshi Murata

Keyword(s):

Artificial Intelligence ◽

Social Networks ◽

Network Science ◽

Influence Maximization ◽

Disease Spread ◽

Temporal Networks ◽

Epidemic Modeling ◽

Influential Individuals

AbstractOngoing COVID-19 pandemic poses many challenges to the research of artificial intelligence. Epidemics are important in network science for modeling disease spread over networks of contacts between individuals. To prevent disease spread, it is desirable to introduce prioritized isolation of the individuals contacting many and unspecified others, or connecting different groups. Finding such influential individuals in social networks, and simulating the speed and extent of the disease spread are what we need for combating COVID-19. This article focuses on the following topics, and discusses some of the traditional and emerging research attempts: (1) topics related to epidemics in network science, such as epidemic modeling, influence maximization and temporal networks, (2) recent research of network science for COVID-19 and (3) datasets and resources for COVID-19 research.

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Using swarm intelligence algorithms to detect influential individuals for influence maximization in social networks

Expert Systems with Applications ◽

10.1016/j.eswa.2018.07.038 ◽

2018 ◽

Vol 114 ◽

pp. 224-236 ◽

Cited By ~ 12

Author(s):

Aybike ŞİMŞEK ◽

Resul KARA

Keyword(s):

Social Networks ◽

Swarm Intelligence ◽

Influence Maximization ◽

Influential Individuals

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On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks

Complexity ◽

10.1155/2017/5049836 ◽

2017 ◽

Vol 2017 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

Xiaodong Liu ◽

Xiangke Liao ◽

Shanshan Li ◽

Si Zheng ◽

Bin Lin ◽

...

Keyword(s):

Social Networks ◽

Real World ◽

Search Space ◽

Influence Maximization ◽

Viral Marketing ◽

Maximization Problem ◽

Dynamic Social Networks ◽

Influence Spread ◽

The Impact ◽

Influential Individuals

Influence maximization problem aims to identify the most influential individuals so as to help in developing effective viral marketing strategies over social networks. Previous studies mainly focus on designing efficient algorithms or heuristics on a static social network. As a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time. In this paper, we propose an incremental approach, IncInf, which can efficiently locate the top-K influential individuals in evolving social networks based on previous information instead of calculation from scratch. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to narrow the search space into nodes experiencing major increases or with high degrees. To evaluate the efficiency and effectiveness, we carried out extensive experiments on real-world dynamic social networks: Facebook, NetHEPT, and Flickr. Experimental results demonstrate that, compared with the state-of-the-art static algorithm, IncInf achieves remarkable speedup in execution time while maintaining matching performance in terms of influence spread.

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The Spatial Dimensions of Social Networks

The Oxford Handbook of Social Networks ◽

10.1093/oxfordhb/9780190251765.013.41 ◽

2020 ◽

pp. 367-383

Author(s):

Zachary P. Neal

Keyword(s):

Social Networks ◽

Topological Space ◽

Network Science ◽

Spatial Dimensions

The first law of geography holds that everything is related to everything else, but near things are more related than distant things, where distance refers to topographical space. If a first law of network science exists, it would similarly hold that everything is related to everything else, but near things are more related than distant things, but where distance refers to topological space. Frequently these two laws collide, together holding that everything is related to everything else, but topographically and topologically near things are more related than topographically and topologically distant things. The focus of the spatial study of social networks lies in exploring a series of questions embedded in this combined law of geography and networks. This chapter explores the questions that have been asked and the answers that have been offered at the intersection of geography and networks.

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