Uncover Topic-Sensitive Information Diffusion Networks

Nan Du, Le Song, Hyenkyun Woo, Hongyuan Zha
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, PMLR 31:229-237, 2013.

Abstract

Analyzing the spreading patterns of memes with respect to their topic distributions and the underlying diffusion network structures is an important task in social network analysis. This task in many cases becomes very challenging since the underlying diffusion networks are often hidden, and the topic specific transmission rates are unknown either. In this paper, we propose a continuous time model, TopicCascade, for topic-sensitive information diffusion networks, and infer the hidden diffusion networks and the topic dependent transmission rates from the observed time stamps and contents of cascades. One attractive property of the model is that its parameters can be estimated via a convex optimization which we solve with an efficient proximal gradient based block coordinate descent (BCD) algorithm. In both synthetic and real-world data, we show that our method significantly improves over the previous state-of-the-art models in terms of both recovering the hidden diffusion networks and predicting the transmission times of memes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v31-du13a, title = {Uncover Topic-Sensitive Information Diffusion Networks}, author = {Du, Nan and Song, Le and Woo, Hyenkyun and Zha, Hongyuan}, booktitle = {Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics}, pages = {229--237}, year = {2013}, editor = {Carvalho, Carlos M. and Ravikumar, Pradeep}, volume = {31}, series = {Proceedings of Machine Learning Research}, address = {Scottsdale, Arizona, USA}, month = {29 Apr--01 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v31/du13a.pdf}, url = {https://proceedings.mlr.press/v31/du13a.html}, abstract = {Analyzing the spreading patterns of memes with respect to their topic distributions and the underlying diffusion network structures is an important task in social network analysis. This task in many cases becomes very challenging since the underlying diffusion networks are often hidden, and the topic specific transmission rates are unknown either. In this paper, we propose a continuous time model, TopicCascade, for topic-sensitive information diffusion networks, and infer the hidden diffusion networks and the topic dependent transmission rates from the observed time stamps and contents of cascades. One attractive property of the model is that its parameters can be estimated via a convex optimization which we solve with an efficient proximal gradient based block coordinate descent (BCD) algorithm. In both synthetic and real-world data, we show that our method significantly improves over the previous state-of-the-art models in terms of both recovering the hidden diffusion networks and predicting the transmission times of memes.} }
Endnote
%0 Conference Paper %T Uncover Topic-Sensitive Information Diffusion Networks %A Nan Du %A Le Song %A Hyenkyun Woo %A Hongyuan Zha %B Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2013 %E Carlos M. Carvalho %E Pradeep Ravikumar %F pmlr-v31-du13a %I PMLR %P 229--237 %U https://proceedings.mlr.press/v31/du13a.html %V 31 %X Analyzing the spreading patterns of memes with respect to their topic distributions and the underlying diffusion network structures is an important task in social network analysis. This task in many cases becomes very challenging since the underlying diffusion networks are often hidden, and the topic specific transmission rates are unknown either. In this paper, we propose a continuous time model, TopicCascade, for topic-sensitive information diffusion networks, and infer the hidden diffusion networks and the topic dependent transmission rates from the observed time stamps and contents of cascades. One attractive property of the model is that its parameters can be estimated via a convex optimization which we solve with an efficient proximal gradient based block coordinate descent (BCD) algorithm. In both synthetic and real-world data, we show that our method significantly improves over the previous state-of-the-art models in terms of both recovering the hidden diffusion networks and predicting the transmission times of memes.
RIS
TY - CPAPER TI - Uncover Topic-Sensitive Information Diffusion Networks AU - Nan Du AU - Le Song AU - Hyenkyun Woo AU - Hongyuan Zha BT - Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics DA - 2013/04/29 ED - Carlos M. Carvalho ED - Pradeep Ravikumar ID - pmlr-v31-du13a PB - PMLR DP - Proceedings of Machine Learning Research VL - 31 SP - 229 EP - 237 L1 - http://proceedings.mlr.press/v31/du13a.pdf UR - https://proceedings.mlr.press/v31/du13a.html AB - Analyzing the spreading patterns of memes with respect to their topic distributions and the underlying diffusion network structures is an important task in social network analysis. This task in many cases becomes very challenging since the underlying diffusion networks are often hidden, and the topic specific transmission rates are unknown either. In this paper, we propose a continuous time model, TopicCascade, for topic-sensitive information diffusion networks, and infer the hidden diffusion networks and the topic dependent transmission rates from the observed time stamps and contents of cascades. One attractive property of the model is that its parameters can be estimated via a convex optimization which we solve with an efficient proximal gradient based block coordinate descent (BCD) algorithm. In both synthetic and real-world data, we show that our method significantly improves over the previous state-of-the-art models in terms of both recovering the hidden diffusion networks and predicting the transmission times of memes. ER -
APA
Du, N., Song, L., Woo, H. & Zha, H.. (2013). Uncover Topic-Sensitive Information Diffusion Networks. Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 31:229-237 Available from https://proceedings.mlr.press/v31/du13a.html.

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