Decoupling Homophily and Reciprocity with Latent Space Network Models

Jiasen Yang, Vinayak Rao, Jennifer Neville
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:261-270, 2017.

Abstract

Networks form useful representations of data arising in various physical and social domains. In this work, we consider dynamic networks such as communication networks in which links connecting pairs of nodes appear over continuous time. We adopt a point process- based approach, and study latent space models which embed the nodes into Euclidean space. We propose models to capture two different as- pects of dynamic network data: (i) commu- nication occurs at a higher rate between indi- viduals with similar features (homophily), and (ii) individuals tend to reciprocate communi- cations from other nodes, but in a manner that varies across individuals. Our framework mar- ries ideas from point process models, includ- ing Poisson and Hawkes processes, with ideas from latent space models of static networks. We evaluate our models over a range of tasks on real-world datasets and show that a dual la- tent space model, which accounts for hetero- geneity in both reciprocity and homophily, sig- nificantly improves performance for both static and dynamic link prediction.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-yang17a, title = {Decoupling Homophily and Reciprocity with Latent Space Network Models}, author = {Yang, Jiasen and Rao, Vinayak and Neville, Jennifer}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {261--270}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/yang17a/yang17a.pdf}, url = {https://proceedings.mlr.press/r15/yang17a.html}, abstract = {Networks form useful representations of data arising in various physical and social domains. In this work, we consider dynamic networks such as communication networks in which links connecting pairs of nodes appear over continuous time. We adopt a point process- based approach, and study latent space models which embed the nodes into Euclidean space. We propose models to capture two different as- pects of dynamic network data: (i) commu- nication occurs at a higher rate between indi- viduals with similar features (homophily), and (ii) individuals tend to reciprocate communi- cations from other nodes, but in a manner that varies across individuals. Our framework mar- ries ideas from point process models, includ- ing Poisson and Hawkes processes, with ideas from latent space models of static networks. We evaluate our models over a range of tasks on real-world datasets and show that a dual la- tent space model, which accounts for hetero- geneity in both reciprocity and homophily, sig- nificantly improves performance for both static and dynamic link prediction.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Decoupling Homophily and Reciprocity with Latent Space Network Models %A Jiasen Yang %A Vinayak Rao %A Jennifer Neville %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-yang17a %I PMLR %P 261--270 %U https://proceedings.mlr.press/r15/yang17a.html %V R15 %X Networks form useful representations of data arising in various physical and social domains. In this work, we consider dynamic networks such as communication networks in which links connecting pairs of nodes appear over continuous time. We adopt a point process- based approach, and study latent space models which embed the nodes into Euclidean space. We propose models to capture two different as- pects of dynamic network data: (i) commu- nication occurs at a higher rate between indi- viduals with similar features (homophily), and (ii) individuals tend to reciprocate communi- cations from other nodes, but in a manner that varies across individuals. Our framework mar- ries ideas from point process models, includ- ing Poisson and Hawkes processes, with ideas from latent space models of static networks. We evaluate our models over a range of tasks on real-world datasets and show that a dual la- tent space model, which accounts for hetero- geneity in both reciprocity and homophily, sig- nificantly improves performance for both static and dynamic link prediction. %Z Reissued by PMLR on 04 October 2026.
APA
Yang, J., Rao, V. & Neville, J.. (2017). Decoupling Homophily and Reciprocity with Latent Space Network Models. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:261-270 Available from https://proceedings.mlr.press/r15/yang17a.html. Reissued by PMLR on 04 October 2026.

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