Multi-view hypergraph convolution network for semantic annotation in LBSNs

Dubey, Manisha and Srijith, P K and Desarkar, Maunendra Sankar (2021) Multi-view hypergraph convolution network for semantic annotation in LBSNs. In: 13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021, Virtual, Online.

[img] Text
Proceedings_2021_IEEE_ACM.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy


Semantic characterization of the Point-of-Interest (POI) plays an important role for modeling location-based social networks and various related applications like POI recommendation, link prediction etc. However, semantic categories are not available for many POIs which makes this characterization difficult. Semantic annotation aims to predict such missing categories of POIs. Existing approaches learn a representation of POIs using graph neural networks to predict semantic categories. However, LBSNs involve complex and higher order mobility dynamics. These higher order relations can be captured effectively by employing hypergraphs. Moreover, visits to POIs can be attributed to various reasons like temporal characteristics, spatial context etc. Hence, we propose a Multi-view Hypergraph Convolution Network (Multi-HGCN) where we learn POI representations by considering multiple hypergraphs across multiple views of the data. We build a comprehensive model to learn the POI representation capturing temporal, spatial and trajectory-based patterns among POIs by employing hypergraphs. We use hypergraph convolution to learn better POI representation by using spectral properties of hypergraph. Experiments conducted on three real-world datasets show that the proposed approach outperforms the state-of-the-art approaches. © 2021 ACM.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Srijith, P K
Desarkar, Maunendra Sankar
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: hypergraphs; location-based social networks; semantic annotation
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 09 Sep 2022 06:56
Last Modified: 09 Sep 2022 06:56
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 10505 Statistics for this ePrint Item