Research Article ComparisonandAnalysisofNetworkConstructionMethodsfor SeismicityBasedonComplexNetworks XuanHe , 1 SyedBilalHussainShah, 2 BoWei , 3 andZhengLiu 4 1 College of Medicine & Biological Information Engineering, Northeastern University, Shenyang 110169, China 2 School of Software, Dalian University of Technology, Dalian 116024, China 3 Department of Computer and Information Sciences, Northumbria University, Newcastle Upon Tyne NE1 8ST, UK 4 School of Computer Science & Engineering, Northeastern University, Shenyang 110169, China Correspondence should be addressed to Xuan He; hexuan@bmie.neu.edu.cn Received 22 December 2020; Revised 12 January 2021; Accepted 20 January 2021; Published 2 February 2021 Academic Editor: Wei Wang Copyright©2021XuanHeetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. eapproachofthecomplexnetworkhaswelldescribedseismiccomplexsystems.Inthispaper,thisisthefirsttimethreeclassical network construction methods for seismicity are compared. By using the same dataset from the Southern California Seismic Network, three networks are constructed. ey all present the scale-free, small-world properties, a strength-degree correlation, and an assortative mixing feature. However, they show some differences in the hierarchical clustering feature. On observing the evolution results, three measures show a similar correlation with seismicity dynamics, but one measure shows a different result. ese results show that different network construction methods will present some similarities and differences in network properties. is situation needs to be considered, especially when discussing a predictive indicator of seismicity. 1.Introduction Network science is widely used in many fields in the real world to describe complex systems’ characteristics. In order torepresentacomplexsystemasagraph,nodesareusually usedtorepresentresearchobjects,whileedgesrepresentthe relationships between research objects. Scientists represent complex systems as graphs from different perspectives in various fields, such as brain networks [1], protein-protein networks[2],socialnetworks[3],Internettopology[4],and transportation networks [5–7]. Complex networks prove to be an effective method to study the complex system. Due to some unknown dynamics of the earth’s crust, seismicactivityhasbeenproventobeacomplexsystemwith temporal and spatial characteristics [8]. Recently, seismic complexsystemshavebeendescribedbytheapproachofthe complexnetwork[9].emostsignificantadvantageisthat we no longer study seismic activity from some small local areas or study one big shock but consider the relationships between seismic events from a broader geographical scope. Most of the proposed methods [9–13] can construct a complex earthquake network only from the main elements ofmagnitude,time,andlocationandhaveachievedprecious results. ey discovered that the earthquake network is scale-free and small-world. ey also discovered that the networks’ topological characteristics change over time, correspondingtothelargeearthquakes[14–16].Resaeietal. [17] found that the PageRank value is an appropriate alarming clue before the event’s occurrence, which is worthwhile in hazard probabilistic evaluation of earth- quakes. However, the discovery of these laws is based on different network construction methods. Our recent study found that the conclusions drawn by different network constructionmethodswillbedifferenttosomeextent.Asfar as we know, no researchers have compared and analyzed these differences. Abe and SuzukiAbe proposed the earthquake network constructionmethodforthefirsttimein2004[9].eyfirst dividedthegeographicalregionintomanysmallequal-sized cells.Ifanyeventoccurredinthecell,thecellisrepresented by a node in the network. Two successive events defined an edgefromtheformertothelatterbetweentwonodes.Inthis Hindawi Complexity Volume 2021, Article ID 6691880, 11 pages https://doi.org/10.1155/2021/6691880