Latent Space Model for Multi-Modal Social Data
By Yoon-Sik Cho, Greg Ver Steeg, Emilio Ferrara, Aram Galstyan. 2015
22 citations recorded by the source corpus.
With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Transportation
How researchers see this paper
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In Artificial Intelligence
- Xiangnan He
- Tat-seng Chua
- Xiang Wang
- Hanwang Zhang
- Lizi Liao
- Aram Galstyan
- Fred Morstatter
- Liqiang Nie
In Statistical and Nonlinear Physics
- Tat-seng Chua
- Hanwang Zhang
- Aram Galstyan
- Fred Morstatter
- Nanyun Peng
- Lizi Liao
- Ninareh Mehrabi
- Xiangnan He
In Transportation
Citations in the corpus
- Debiasing Community Detection: The Importance of Lowly Connected Nodes (2019, methodology): “In addition to CESNA, other methods have been proposed, such as Block-LDA [9], PAICAN [10], shared latent space models such as CLSM [7], and embedding-based approaches like LANE [11], which utilize node attributes in order to detect communities, or ELAINE [12], which utilizes”
- Attributed Social Network Embedding (2017, methodology): “To address this shortcoming, [27], [28] were designed to impose additional constraints on the generative model that”