Density-based place clustering in geo-social networks

Shi, Jieming; Mamoulis, Nikos; Wu, Dingming; Cheung, David W.

doi:10.1145/2588555.2610497

Cited by 67 publications

(29 citation statements)

References 35 publications

(38 reference statements)

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“…In phase 1, this study implements improved self-organized segmentation algorithms to group the neighborhood into clusters. The improved algorithms extend previous work by Shi [15], [16] that utilized DBSCAN algorithms [17] for clustering in a geosocial network. In the improved algorithms, distance between residents is calculated using Haversine formula [18] due to higher accuracy for great-circle distance.…”

Section: A Design Flowsupporting

confidence: 59%

Location-based Access Control in Neighborhood Geosocial Network: a Case of Emergency Assistance

Anawar*,

Loong,

Harum

et al. 2020

IJRTE

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To enforce safer virtual neighborhood environment, access control is essential to specify privileges or access rights to resources in neighborhood geosocial network applications. In the case of emergency circumstances, several concerns in implementing access control have been identified: (1) inefficient message sending to irrelevant recipients (2) location spoofing (3) gossip and rumors due to disclosing information to wrong audience. To address such potential risks, access control specifically tailored for neighborhood geosocial network is required. The objective of this paper is two-fold: First, to propose an access control using density-based and position-based location conditions for neighborhood geosocial network. Second, to design mySOS, a proof-of-concept prototype that incorporate the proposed access control to improve access control for emergency assistance in neighborhood geosocial network. To achieve the objectives, a combination of proposed self-organized segmentation algorithms (density-based) and resident engagement incentives (position-based) are implemented. This study contributes to a new way for access control that will determine user profile in accessing emergency assistance features in a neighborhood geosocial network application.

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Section: A Design Flowsupporting

confidence: 59%

Location-based Access Control in Neighborhood Geosocial Network: a Case of Emergency Assistance

Anawar*,

Loong,

Harum

et al. 2020

IJRTE

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“…Our goal is to look for quantities that capture some degree of closeness between members of the groups. Social networks depict different types of features that can be used to organize users into groups based on their behavior such as historical visits, temporal features, categorical preferences and social distance of the visited places [10], [8], [18]. In this work we exploit the most popular features in the literature, namely the spatial and attendance features.…”

Section: ) Feature-based Groupsmentioning

confidence: 99%

Personalized Event Recommendations Using Social Networks

Boutsis

Karanikolaou

Kalogeraki

2015

2015 16th IEEE International Conference on Mobile Data Management

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In recent years we have observed a significant increase in the popularity of location-based social networks for exchanging news and experiences, sharing location information, or publishing real world events. One important challenge in such networks is to understand human crowd mobility behavior based on user social activities and interactions. In this paper we introduce PRESENT, our middleware that utilizes a Mixed Markov Model to extract the behavioral patterns of the users in social groups, to make personalized event recommendations. Our detailed experimental evaluation, using data from the Meetup location-based social network, illustrates that our approach is efficient, practical and achieves an average prediction for the user attendance of over 73%.

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“…SimRank is a popular similarity measure between nodes in a graph, with numerous potential applications, e.g., in recommendation systems [26], schema matching [25], spam detection [2], and graph mining [13,19,29]. The main idea of SimRank is that two nodes that are referenced by many similar nodes are themselves similar to each other.…”

Section: Introductionmentioning

confidence: 99%

Realtime index-free single source SimRank processing on web-scale graphs

et al. 2020

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Given a graph G and a node u ∈ G, a single source Sim-Rank query evaluates the similarity between u and every node v ∈ G. Existing approaches to single source SimRank computation incur either long query response time, or expensive pre-computation, which needs to be performed again whenever the graph G changes. Consequently, to our knowledge none of them is ideal for scenarios in which (i) query processing must be done in realtime, and (ii) the underlying graph G is massive, with frequent updates.Motivated by this, we propose SimPush, a novel algorithm that answers single source SimRank queries without any pre-computation, and at the same time achieves significantly higher query processing speed than even the fastest known index-based solutions. Further, SimPush provides rigorous result quality guarantees, and its high performance does not rely on any strong assumption of the underlying graph. Specifically, compared to existing methods, SimPush employs a radically different algorithmic design that focuses on (i) identifying a small number of nodes relevant to the query, and subsequently (ii) computing statistics and performing residue push from these nodes only.We prove the correctness of SimPush, analyze its time complexity, and compare its asymptotic performance with that of existing methods. Meanwhile, we evaluate the practical performance of SimPush through extensive experiments on 9 real datasets. The results demonstrate that SimPush consistently outperforms all existing solutions, often by over an order of magnitude. In particular, on a commodity machine, SimPush answers a single source SimRank query on a web graph containing over 133 million nodes and 5.4 billion edges in under 62 milliseconds, with 0.00035 empirical error, while the fastest index-based competitor needs 1.18 seconds.

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Density-based place clustering in geo-social networks

Cited by 67 publications

References 35 publications

Location-based Access Control in Neighborhood Geosocial Network: a Case of Emergency Assistance

Location-based Access Control in Neighborhood Geosocial Network: a Case of Emergency Assistance

Personalized Event Recommendations Using Social Networks

Realtime index-free single source SimRank processing on web-scale graphs

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