Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same cluster having high similarity and points in different clusters having low similarity. In this paper, we derive new cost functions for spectral clustering based on measures of error between a given partition and a solutio...

In this paper, we investigate the problem of (k,r)-core which intends to find cohesive subgraphs on social networks considering both user engagement and similarity perspectives. In particular, we adopt the popular concept of k-core to guarantee the engagement of the users (vertices) in a group (subgraph) where each vertex in a (k,r)-core connects to at least k other vertices. Meanwhile, we also...

Similarity measures do not typically capture subjective elements of perception similarity. Our research contributes an experimental methodology for validating and learning similarity computation algorithms against human perceptions in domains like Art emotions. In this paper, we explain the first experiment to check our hypotheses methodology. We have obtained promising results that differences...

Efficiently detecting near duplicate resources is an important task when integrating information from various sources and applications. Once detected, near duplicate resources can be grouped together, merged, or removed, in order to avoid repetition and redundancy, and to increase the diversity in the information provided to the user. In this paper, we introduce an approach for efficient semant...

The need for a similarity measure for comparing two drawings of graphs arises in problems such as interactive graph drawing and the indexing or browsing of large sets of graphs. Many applications have been based on intuitive ideas of what makes two drawings look similar — for example, the idea that vertex positions should not change much. In this paper, we formally define several of these intui...

Many applications involve matching two graphs in order to identify their common features and compute their similarity. In this paper, we address the problem of computing a graph similarity measure based on a multivalent graph matching and which is generic in the sense that other well known graph similarity measures can be viewed as special cases of it. We propose and compare two different kinds...

We consider the problem of processing similarity queries over a set of top-k rankings where the query ranking and the similarity threshold are provided at query time. Spearman’s Footrule distance is used to compute the similarity between rankings, considering how well rankings agree on the positions (ranks) of ranked items (i.e., the L1 distance). This setup allows the application of metric ind...

A method is proposed for creating vector space representations of documents based on modeling target inter-document similarity values. The target similarity values are assumed to capture semantic relationships, or associations, between the documents. The vector representations are chosen so that the inner product similarities between document vector pairs closely match their target inter-docume...

We present in this paper SEMILAR, the SEMantic simILARity toolkit. SEMILAR implements a number of algorithms for assessing the semantic similarity between two texts. It is available as a Java library and as a Java standalone ap-plication offering GUI-based access to the implemented semantic similarity methods. Furthermore, it offers facilities for manual se-mantic similarity annotation by exper...

In this paper, we focus on similarity searching for similar acoustic data over unstructured decentralised P2P networks. Similarity is measured in terms of time warping, which can cope with distortion that is naturally present when ‘query by content’ is performed. We propose a novel framework, which takes advantage of the absence of overhead in unstructured P2P networks and minimises the require...