Predicting the dynamics of social circles in ego networks using pattern analysis and <scp>GA</scp> K‐means clustering

Agarwal, Vinti; Bharadwaj, Kamal Kant

doi:10.1002/widm.1150

Cited by 12 publications

(4 citation statements)

References 61 publications

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“…We chose unsupervised learning to overcome the limitations of a priori assumptions of connectivity patterns. Machine learning techniques have been widely used to study network-based data for different purposes, such as finding a prevalent subgraph pattern (Cook & Holder, 2006), classifying or identifying different members (nodes) from a communication/social network (Alsayat & El-Sayed, 2016;Nurek & Michalski, 2020), or measuring dynamics in networks (Agarwal & Bharadwaj, 2015).…”

Section: Choosing a Clustering Algorithmmentioning

confidence: 99%

Characteristics of Jetters and Little Boxes: An Extensibility Study Using the Neighborhood Connectivity Survey

Liang

Lee²,

Chen³

et al. 2022

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Individuals connect to sets of places through travel, migration, telecommunications, and social interactions. This set of multiplex network connections comprises an individual’s “extensibility,” a human geography term that qualifies one’s geographic reach as locally‐focused or globally extensible. Here we ask: Are there clear signals of global vs. local extensibility? If so, what demographic and social life factors correlate with each type of pattern? To answer these questions, we use data from the Neighborhood Connectivity Survey conducted in Akron, Ohio, State College, Pennsylvania, and Philadelphia, Pennsylvania (global sample N = 950; in model n = 903). Based on the location of a variety of connections (travel, phone call patterns, locations of family, migration, etc.), we found that individuals fell into one of four different typologies: (a) hyperlocal, (b) metropolitan, (c) mixed‐many, and (d) regional‐few. We tested whether individuals in each typology had different levels of local social support and different sociodemographic characteristics. We found that respondents who are white, married, and have higher educational attainment are significantly associated with more connections to a wider variety of places (more global connections), while respondents who are Black/African American, single, and with a high school level educational attainment (or lower) have more local social and spatial ties. Accordingly, the “urban poor” may be limited in their ability to interact with a variety of places (yielding a wide set of geographic experiences and influences), suggesting that wide extensibility may be a mark of privileged circumstances and heightened agency.

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Section: Choosing a Clustering Algorithmmentioning

confidence: 99%

Characteristics of Jetters and Little Boxes: An Extensibility Study Using the Neighborhood Connectivity Survey

Liang

Lee²,

Chen³

et al. 2022

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“…These methods are applicable only to evaluation ratings, and they cannot effectively solve the problem of data sparsity. Agarwal and Bharadwaj [25] use GA to predict unknown evaluation values based on evaluation values and trust, to improve the accuracy of the recommendation results. Bedi and Sharma [26] proposed a method called the trust-based ant recommendation system (TAS), which metaphorizes the trust relationship between the users to the biology of ant colonies.…”

Section: User-based Collaborative Filteringmentioning

confidence: 99%

A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships

Ma¹,

Wang²,

Qin³

2020

Symmetry

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Low-rank tensor factorization can not only mine the implicit relationships between data but also fill in the missing data when working with complex data. Compared with the traditional collaborative filtering (CF) algorithm, the changes are essentially proposed, from traditional matrix analysis to three-dimensional spatial analysis. Based on low-rank tensor factorization, this paper proposes a recommendation model that comprehensively considers local information and global information, in other words, combining the similarity between trust users and low-rank tensor factorization. First, the similarity between trusted users is measured to capture local information between users by trusting similar preferences of users when selecting items. Then, the users’ similarity is integrated into the tensor, and the low-rank tensor factorization is used to better maintain and describe the internal structure of the data to obtain global information. Furthermore, based on the idea of the alternating least squares method, the conjugate gradient (CG) optimization algorithm for the model of this paper is designed. The local and global information is used to generate the optimal expected result in an iterative process. Finally, we conducted a large number of comparative experiments on the Ciao dataset and the FilmTrust dataset. Experimental results show that the algorithm has less precision loss under the data set with lower density. Thus, not only can a perfect compromise between accuracy and coverage be achieved, but also the computational complexity can be reduced to meet the need for real-time results.

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“…The GA k‐means algorithm is applied to the outcome of factorization (latent factor matrix F ) to obtain desirable communities. The reasons that underpin the adoption of a GA‐based approach are it performs a global search in the problem space and can be easily hybridized and extended to suit any problem structure …”

Section: The Proposed Model For Discovering Communities In Multi‐relamentioning

confidence: 99%

Identifying community structure in a multi‐relational network employing non‐negative tensor factorization and GA k‐means clustering

Verma

Bharadwaj

2016

WIREs Data Min & Knowl

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The ubiquity of social networking sites leads to the generation of rich social media content. Community discovery is one of the significant tools in the analysis of social media data that is often multi-relational due to diverse forms of user interactions. Although there has been extensive research devoted to community discovery, most of it is restricted to single-relational networks. However, focus has been shifted to multi-relational networks in the recent years. In this study, we aim to discover communities in the multi-relational networks through relational learning. Our main focus is the utilization of non-negative tensor factorization and GA k-means clustering for community discovery. In order to incorporate the relational characteristics of the data in the learning methodology, tensors are used to model the multi-relational network. Tensor factorization reveals the latent features of the data and shows state-of-the-art results for multi-relational learning. Once the implicit information is obtained by factorization, we apply a GA k-means clustering algorithm for community discovery. Experiments are performed on synthetic as well as real datasets. The results obtained are quite promising and clearly demonstrate the effectiveness of our proposed scheme.

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Predicting the dynamics of social circles in ego networks using pattern analysis and GA K‐means clustering

Cited by 12 publications

References 61 publications

Characteristics of Jetters and Little Boxes: An Extensibility Study Using the Neighborhood Connectivity Survey

Characteristics of Jetters and Little Boxes: An Extensibility Study Using the Neighborhood Connectivity Survey

A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships

Identifying community structure in a multi‐relational network employing non‐negative tensor factorization and GA k‐means clustering

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