Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):1301-1309, 2013.
Abstract
How does the activity of one person affect that of another person? Does the strength of influence remain periodic or decay exponentially over time? In this paper, we study these critical questions in social network analysis quantitatively under the framework of multi-dimensional Hawkes processes. In particular, we focus on the nonparametric learning of the triggering kernels, and propose an algorithm \sf MMEL that combines the idea of decoupling the parameters through constructing a tight upper-bound of the objective function and application of Euler-Lagrange equations for optimization in infinite dimensional functional space. We show that the proposed method performs significantly better than alternatives in experiments on both synthetic and real world datasets.
@InProceedings{pmlr-v28-zhou13,
title = {Learning Triggering Kernels for Multi-dimensional Hawkes Processes},
author = {Ke Zhou and Hongyuan Zha and Le Song},
booktitle = {Proceedings of the 30th International Conference on Machine Learning},
pages = {1301--1309},
year = {2013},
editor = {Sanjoy Dasgupta and David McAllester},
volume = {28},
number = {3},
series = {Proceedings of Machine Learning Research},
address = {Atlanta, Georgia, USA},
month = {17--19 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v28/zhou13.pdf},
url = {http://proceedings.mlr.press/v28/zhou13.html},
abstract = {How does the activity of one person affect that of another person? Does the strength of influence remain periodic or decay exponentially over time? In this paper, we study these critical questions in social network analysis quantitatively under the framework of multi-dimensional Hawkes processes. In particular, we focus on the nonparametric learning of the triggering kernels, and propose an algorithm \sf MMEL that combines the idea of decoupling the parameters through constructing a tight upper-bound of the objective function and application of Euler-Lagrange equations for optimization in infinite dimensional functional space. We show that the proposed method performs significantly better than alternatives in experiments on both synthetic and real world datasets. }
}
%0 Conference Paper
%T Learning Triggering Kernels for Multi-dimensional Hawkes Processes
%A Ke Zhou
%A Hongyuan Zha
%A Le Song
%B Proceedings of the 30th International Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2013
%E Sanjoy Dasgupta
%E David McAllester
%F pmlr-v28-zhou13
%I PMLR
%J Proceedings of Machine Learning Research
%P 1301--1309
%U http://proceedings.mlr.press
%V 28
%N 3
%W PMLR
%X How does the activity of one person affect that of another person? Does the strength of influence remain periodic or decay exponentially over time? In this paper, we study these critical questions in social network analysis quantitatively under the framework of multi-dimensional Hawkes processes. In particular, we focus on the nonparametric learning of the triggering kernels, and propose an algorithm \sf MMEL that combines the idea of decoupling the parameters through constructing a tight upper-bound of the objective function and application of Euler-Lagrange equations for optimization in infinite dimensional functional space. We show that the proposed method performs significantly better than alternatives in experiments on both synthetic and real world datasets.
TY - CPAPER
TI - Learning Triggering Kernels for Multi-dimensional Hawkes Processes
AU - Ke Zhou
AU - Hongyuan Zha
AU - Le Song
BT - Proceedings of the 30th International Conference on Machine Learning
PY - 2013/02/13
DA - 2013/02/13
ED - Sanjoy Dasgupta
ED - David McAllester
ID - pmlr-v28-zhou13
PB - PMLR
SP - 1301
DP - PMLR
EP - 1309
L1 - http://proceedings.mlr.press/v28/zhou13.pdf
UR - http://proceedings.mlr.press/v28/zhou13.html
AB - How does the activity of one person affect that of another person? Does the strength of influence remain periodic or decay exponentially over time? In this paper, we study these critical questions in social network analysis quantitatively under the framework of multi-dimensional Hawkes processes. In particular, we focus on the nonparametric learning of the triggering kernels, and propose an algorithm \sf MMEL that combines the idea of decoupling the parameters through constructing a tight upper-bound of the objective function and application of Euler-Lagrange equations for optimization in infinite dimensional functional space. We show that the proposed method performs significantly better than alternatives in experiments on both synthetic and real world datasets.
ER -
Zhou, K., Zha, H. & Song, L.. (2013). Learning Triggering Kernels for Multi-dimensional Hawkes Processes. Proceedings of the 30th International Conference on Machine Learning, in PMLR 28(3):1301-1309
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