Tracking Group Movement in Location Based Social Networks

Kannangara, Sameera; Xie, Hairuo; Tanin, Egemen; Harwood, Aaron; Karunasekera, Shanika

doi:10.1145/3397536.3422211

Proceedings of the 28th International Conference on Advances in Geographic Information Systems 2020

DOI: 10.1145/3397536.3422211

|View full text |Cite

Tracking Group Movement in Location Based Social Networks

Sameera Kannangara

Hairuo Xie

Egemen Tanin

et al.

Abstract: We study the problem of tracking the movement of groups using sparse trajectory data extracted from Location Based Social Networks (LBSNs). Tracking group movement using LBSN data is challenging because the data may contain a large amount of noise due to the lack of stability in group entity, spatial extent and posting time. We propose a first-of-its-kind solution, Group Kalman Filter (GKF), which aims to improve the effectiveness of group tracking by predicting the spatial properties of groups with a group mo… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2023

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Predicting Co-movement patterns in mobility data

et al. 2022

View full text Add to dashboard Cite

Predictive analytics over mobility data is of great importance since it can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example is anticipated location prediction, where the goal is to predict the future location of a moving object, given a look-ahead time. What is even more challenging is to be able to accurately predict collective behavioural patterns of movement, such as co-movement patterns as well as their course over time. In this paper, we address the problem of Online Prediction of Co-movement Patterns. Furthermore, in order to be able to calculate the accuracy of our solution, we propose a co-movement pattern similarity measure, which facilitates the comparison between the predicted clusters and the actual ones. Finally, we calculate the clusters’ evolution through time (survive, split, etc.) and compare the cluster evolution predicted by our framework with the actual one. Our experimental study uses two real-world mobility datasets from the maritime and urban domain, respectively, and demonstrates the effectiveness of the proposed framework.

show abstract

Predicting Co-movement patterns in mobility data

et al. 2022

View full text Add to dashboard Cite

show abstract

A Personalized Explainable Learner Implicit Friend Recommendation Method

Zhou

Lin

et al. 2023

Data Sci. Eng.

View full text Add to dashboard Cite

With the rapid development of social networks, academic social networks have attracted increasing attention. In particular, providing personalized recommendations for learners considering data sparseness and cold-start scenarios is a challenging task. An important research topic is to accurately discover potential friends of learners to build implicit learning groups and obtain personalized collaborative recommendations of similar learners according to the learning content. This paper proposes a personalized explainable learner implicit friend recommendation method (PELIRM). Methodologically, PELIRM utilizes the learner's multidimensional interaction behavior in social networks to calculate the degrees of trust between learners and applies the three-degree influence theory to mine the implicit friends of learners. The similarity of research interests between learners is calculated by cosine and term frequency–inverse document frequency. To solve the recommendation problem for cold-start learners, the learner's common check-in IP is used to obtain the learner's location information. Finally, the degree of trust, similarity of research interests, and geographic distance between learners are combined as ranking indicators to recommend potential friends for learners and give multiple interpretations of the recommendation results. By verifying and evaluating the proposed method on real data from Scholar.com, the experimental results show that the proposed method is reliable and effective in terms of personalized recommendation and explainability.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Tracking Group Movement in Location Based Social Networks

Cited by 2 publications

References 17 publications

Predicting Co-movement patterns in mobility data

Predicting Co-movement patterns in mobility data

A Personalized Explainable Learner Implicit Friend Recommendation Method

Contact Info

Product

Resources

About