Collaborative Similarity Measure Research Articles

SAG Cluster: An unsupervised graph clustering based on collaborative similarity for community detection in complex networks

Many real-world social networks such as brain graph, protein structure, food web, transportation system, World Wide Web, online social networks exist in the form of a complex network. In such complex networks, pattern identification or community detection requires extra effort in which identifying community is a significant problem in various research areas. Most of the clustering methods on graphs predominantly emphasize on the topological structure without considering connectivity between vertices and not bearing in mind the vertex properties/attributes or similarity-based on indirectly connected vertices. A novel clustering algorithm SAG-Cluster with K-medoids framework presented for detecting communities using a collaborative similarity measure which considers attribute importance in case the pair of disconnected nodes. A novel path strategy using classic Basel problem for the indirectly connected node as well as balanced attribute similarity and distance function is proposed. On two real data sets, experimental results show the effectiveness of SAG-Cluster with the comparison of other relevant methods.

A graph clustering method for community detection in complex networks

Abstract Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.

A multi-user perspective for personalized email communities

Email classification and prioritization expert systems have the potential to automatically group emails and users as communities based on their communication patterns, which is one of the most tedious tasks. The exchange of emails among users along with the time and content information determine the pattern of communication. The intelligent systems extract these patterns from an email corpus of single or all users and are limited to statistical analysis. However, the email information revealed in those methods is either constricted or widespread, i.e. single or all users respectively, which limits the usability of the resultant communities. In contrast to extreme views of the email information, we relax the aforementioned restrictions by considering a subset of all users as multi-user information in an incremental way to extend the personalization concept. Accordingly, we propose a multi-user personalized email community detection method to discover the groupings of email users based on their structural and semantic intimacy. We construct a social graph using multi-user personalized emails. Subsequently, the social graph is uniquely leveraged with expedient attributes, such as semantics, to identify user communities through collaborative similarity measure. The multi-user personalized communities, which are evaluated through different quality measures, enable the email systems to filter spam or malicious emails and suggest contacts while composing emails. The experimental results over two randomly selected users from email network, as constrained information, unveil partial interaction among 80% email users with 14% search space reduction where we notice 25% improvement in the clustering coefficient.

Intra graph clustering using collaborative similarity measure

Graph is an extremely versatile data structure in terms of its expressiveness and flexibility to model a range of real life phenomenon. Various networks like social networks, sensor networks and computer networks are represented and stored in the form of graphs. The analysis of these kind of graphs has an immense importance from quite a long time. It is performed from various aspects to get maximum out of such multifaceted information repository. When the analysis is targeted towards finding groups of vertices based on their similarity in a graph, clustering is the most conspicuous option. Previous graph clustering approaches either focus on the topological structure or attributes likeness, however, few recent methods constitutes both aspects simultaneously. Due to enormous computation requirements for similarity estimation, these methods are often suffered from scalability issues. In order to overcome this limitation, we introduce collaborative similarity measure (CSM) for intra-graph clustering. CSM is based on shortest path strategy, instead of all paths, to define structural and semantic relevance among vertices. First, we calculate the pair-wise similarity among vertices using CSM. Second, vertices are grouped together based on calculated similarity under k-Medoid framework. Empirical analysis, based on density, and entropy, proves the efficacy of CSM over existing measures. Moreover, CSM becomes a potential candidate for medium scaled graph analysis due to an order of magnitude less computations.

Collaborative object tracking model with local sparse representation

There existed many visual tracking methods that are based on sparse representation model, most of them were either generative or discriminative, which made object tracking more difficult when objects have undergone large pose change, illumination variation or partial occlusion. To address this issue, in this paper we propose a collaborative object tracking model with local sparse representation. The key idea of our method is to develop a local sparse representation-based discriminative model (SRDM) and a local sparse representation-based generative model (SRGM). In the SRDM module, the appearance of a target is modeled by local sparse codes that can be formed as training data for a linear classifier to discriminate the target from the background. In the SRGM module, the appearance of the target is represented by sparse coding histogram and a sparse coding-based similarity measure is applied to compute the distance between histograms of a target candidate and the target template. Finally, a collaborative similarity measure is proposed for measuring the difference of the two models, and then the corresponding likelihood of the target candidates is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Experiments on some publicly available benchmarks of video sequences showed that our proposed tracker is robust and effective.

An Adaptive Feature-fusion Method for Object Matching over Non-overlapped Scenes

Object matching in non-overlapped scenes of multi-cameras is a challenging task, due to a large number of factors, e.g. complex backgrounds, illumination variance, pose of observed object, viewpoint and image resolutions of different cameras, shadows and occlusions. For an object, matching its observations with variant appearances in such context usually turns to evaluate their similarity over some sophisticatedly chosen image features. We observe that certain feature is usually robust to certain variance, e.g. SIFT is robust to the variance in viewpoint and scale. We mean that incorporating the abilities of a bag of such features would reach a better performance. Based on these observations and insights, we propose an adaptive feature-fusion algorithm. The algorithm, first, evaluates the matching accuracy of four sophisticatedly chosen and well validated features: color histogram, UV chromaticity, major color spectrum and SIFT, using exponential models of entropy as similarity measure. Second, an adaptive fusion algorithm is presented to fuse a bag of features for a collaborative similarity measure. Our approach is shown to be able to adaptively and dynamically reduce the variances of object appearances caused by multiple factors. Experimental results show that our approach applied to human matching reaches a high robustness and matching accuracy in comparison with the previous fusion methods.