Comparing Community Detection Algorithms in Transport Networks via Points of Interest | Zendy

Liping Huang | Zendy; Yongjian Yang | Zendy; Hepeng Gao | Zendy; Xuehua Zhao | Zendy; Zhanwei Du | Zendy

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Comparing Community Detection Algorithms in Transport Networks via Points of Interest

Author(s) -

Liping Huang,

Yongjian Yang,

Hepeng Gao,

Xuehua Zhao,

Zhanwei Du

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2841321

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Passengers travel in transport networks with diverse interests represented by linked points of interest (POIs) and drive urban regions to group into network communities. Previous studies focused on applying community detection methods (CDMs) to discover spatial mobility patterns or using POIs to explain the decision making of human mobility, without comparing the effectiveness of CDMs for detecting network communities. In this paper, we analyze the relationship between POIs and network communities of human mobility over diverse CDMs. Taking the taxi systems of Shanghai and Beijing as case studies, we construct transport networks with urban regions as nodes and the connections between them as links weighted by mobility flows. The spatial communities are identified based on the movement strength among regions. POIs are mapped into nodes in the network and are considered as independent variables for classifying the spatial community categories. Our study suggests that communities detected with two specific CMDs (namely, the Combo algorithm and the Walktrap algorithm) correlate to POIs, and the correlation of the Combo is the best (R² = 0.3 for Shanghai and R² = 0.48 for Beijing). In this regard, this paper can provide valuable insight into understanding the formation of spatial communities and assist in selecting reasonable CDMs.

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