Modeling Relational Events via Latent Classes

Citation

Christopher DuBois, Padhraic Smyth. Modeling Relational Events via Latent Classes. KDD 2010

Online version

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Summary

Many social network activities can be described as a series of dyadic events. An event in this paper is defined as a triple of (sender, receiver, event_type). Authors assume that such events are generated by some latent class and in the paper they proposed a graphical model to identify the latent class as well as dyadic events with the inference implementation of Gibbs sampling and Expectation-Maximization methods.

Methodology

It's assumed that relational events are generated by following process:

• Draw the class distribution ${\bar {\pi }}$ ~ Dirichlet(${\bar {a}}$)
• Draw distributions:

$\theta _{c}$ ~ Dirichlet($\beta$)

$\phi _{c}$ ~ Dirichlet($\gamma$)

$\psi _{c}$ ~ Dirichlet($\delta$)

for all c in {1...C}

• For each event

(a) Draw $c$ ~ Multinomial(${\bar {\pi }}$), the event’s class

(b) Draw $s|c$ ~ Multinomial(${\bar {\theta _{c}}}$), the event’s sender

(c) Draw $r|c$ ~ Multinomial(${\bar {\phi _{c}}}$), the event’s receiver

(d) Draw $a|c$ ~ Multinomial(${\bar {\psi _{c}}}$), the event’s type

It's not hard to work out the likelihood for the data:

Two ways of inference, Gibbs sampling and EM, are implemented in this paper.

Data

ACL Antology

Experimental Result

• Historical Trends in Computational Linguistics

To visualize some trend, they show the probability mass asscociated with various topics over time, plotted as (a smoothed version of) ${\hat {p}}(z|y)$. The topics becoming more prominent are such as classification, probabilistic models, stat. parsing, stat. MT and lex. sem, while the topics declined are computational semantics, conceptual semantics and plan-based dialogue and discourse.

• Is Computational Linguistics Becoming More Applied?

Look at trends over time for some applications such as Machine Translation, Spelling Correction, Dialogue Systems etc and found there is a clear trend toward an increase in applications over time.

• Differences and Similarities Among COLING, ACL and EMNLP

Inferred from the topic entropy, COLING has been historically the broadest of the three conferences; ACL started with a fairly narrow focus, became nearly as broad as COLING during the 1990's but become more narrow again in recent years; EMNLP shows being its status as a "special interest" conference.

From the JS divergence, they showed all of the three conferences are converging to their topics.

Related papers

Blei and Lafferty, ICML2006: David Blei and John D. Lafferty. 2006. Dynamic topic models. ICML.

Wang and McCallum, KDD2006: Xuerui Wang and Andrew McCallum. 2006. Topics over time: a non-Markov continuous-time model of topical trends. In KDD, pages 424–433, New York, NY, USA. ACM.