Applying Community Detection Methods to Cluster Tags in Multimedia Search Results

Multimedia searches often return items that can be categorized into several "topics", allowing users to disambiguate and explore answers more efficiently. In this paper we investigate methods for clustering tags associated with multimedia search results, where each resulting cluster represents a topic computed online for that particular search. We specifically investigate the applicability of community detection algorithms to the tag graph induced from the search results. This type of approach allows us to exploit tag similarity and create ad-hoc topics for each search, without specify the number and sizes of clusters a priori. In this work we experiment with well-known algorithms in this field and propose two new methods based on adaptive island cuts. Using the Social20 dataset (a collection gathered from Flickr) we evaluate several community detection methods, with quantitative analysis of each algorithm in terms of the relative number of communities (which we interpret as topics) that they produce and their sizes, as well as qualitative analysis of topics per human judgement. Our evaluation shows that it is possible to extract ad-hoc topics for search results using community detection, but that different community detection methods produce very different results. In particular, our proposed methods produce more compact and less noisy clusters as well as less relative recall when compared to methods that produce much larger clusters.