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Detection of hotspots (also known as dense subgraphs) in network data is an important data analysis problem due to it's significance in many contemporary applications. Clique-based formulation of this problem employing maximum flow implementation turns out to be an optimization task limiting the solution to be an approximate one. On the other hand, an iterative method building the hotspots (dense...
Nowadays, the classification of graph data has become an important and active research topic in the last decade, which has a wide variety of real world applications, e.g. drug activity predictions and kinase inhibitor discovery. Current research on graph classification focuses on single-label settings. However, in many applications, each graph data can be assigned with a set of multiple labels simultaneously...
The growth of the web has directly influenced the increase in the availability of relational data. One of the key problems in mining such data is computing the similarity between objects with heterogeneous feature types. For example, publications have many heterogeneous features like text, citations, authorship information, venue information, etc. In most approaches, similarity is estimated using...
Mining actor correlations from TV series enables semantic level video understanding and facilitates users to conduct correlation-based query. In this paper, we introduce a graph-based actor correlations mining framework, which serves as the first attempt for effective actor association presentation and concurrence search. We leverage face detection and tracking to locate actors with 2D-PCA detector...
It is well known that Web users create links with different intentions. However, a key question, which is not well studied, is how to categorize the links and how to quantify the strength of the influence of a Web page on another if there is a link between the two linked Web pages. In this paper, we focus on the problem of link semantics analysis, and propose a novel supervised learning approach to...
Finding information about people using search engines is one of the most common activities on the Web. However, search engines usually return a long list of Web pages, which may be relevant to many namesakes, especially given the explosive growth of Web data. To address the challenge caused by name ambiguity in Web people search, this paper proposes a novel graph-based framework, GRAPE (abbr. a graph-based...
Random key graphs form a class of random graphs naturally associated with the random key predistribution scheme of Eschenauer and Gligor. We compute the clustering coefficients of random key graphs, and then compare them with Erdos-Renyi graphs in the many node regime when the expected average degrees are asymptotically equivalent. On the parameter range of practical relevance in wireless sensor networks,...
Sentiment classification is very useful in many applications such as analysis of product reviews in e-business applications and media opinion mining. In this paper, a novel method to tag words sentiment automatically is proposed. In this method, a word association graph is firstly constructed from text corpus, i.e. product reviews, in which each node is a word and if there is an edge between two words,...
With the rapid development of information technology, many applications have to deal with potentially infinite data streams. In such a dynamic context, storing the whole data stream history is unfeasible and providing a high-quality summary is required for decision makers. A practical and consistent summarization method is the extraction of the frequent itemsets over temporal windows. Nevertheless,...
In this paper, we present a new method to extract product entity from Chinese customer reviews. The approach requires no segmentation, no domain dictionary and little prior domain knowledge, which is more suitable for domain with resource-limited. Quite different from the previous work, the proposed method first get the entity candidates use a general version bootstrapping algorithm and then distribute...
This paper proposes a new text-based question answering system. It models knowledge in documents and questions with conceptual graph formalism (CGF). To prepare knowledge to be modeled, natural language processing is applied to the text using OpenNLP, and then syntactic and semantic information is realized using VerbNet and WordNet. We handle different types of questions, especially questions constructed...
Protein binding sites are often represented by means of graphs capturing their most important geometrical and physicochemical properties. Searching for structural similarities and identifying functional relationships between them can thus be reduced to matching their corresponding graph descriptors. In this paper, we propose a method for the structural analysis of protein binding sites that makes...
The inter-domain role mapping is a basic method for facilitating interoperation in RBAC-based collaborating environments, where each domain employs role based access control (RBAC) to specify access control policies. Prior to concrete interoperation, one important problem is to establish role mappings. Two issues are involved in the establishing process. The first one is to generate role mappings...
The graphical text representation method such as Conceptual Graphs (CGs) attempts to capture the structure and semantics of documents. As such, they are the preferred text representation approach for a wide range of problems namely in natural language processing, information retrieval and text mining. In a number of these applications, it is necessary to measure the dissimilarity (or similarity) between...
This paper presents our results with the investigation of decentralized data dependency analysis among concurrently executing processes in a service-oriented environment. Distributed Process Execution Agents (PEXAs) are responsible for controlling the execution of processes that are composed of Web services. PEXAs are also associated with specific distributed sites for the purpose of capturing data...
This paper proposes a logical framework that visualizes worker's system awareness. Generally, this awareness is difficult to compile in a educational book, and is seldom communicated in verbally. To show broad utility of system awareness, we need to show operator's conceptual structure of system operating. We firstly carry out operation analysis of graph drawing task using a spreadsheet software....
Random key graphs are random graphs induced by the random key predistribution scheme of Eschenauer and Gligor under the assumption of full visibility. For this class of random graphs we show the existence of a zero-one law for the appearance of triangles, and identify the corresponding critical scaling. This is done by applying the method of first and second moments to the number of triangles in the...
Let D = (V,A) be a digraph. A kernel of D is an independent set S of vertices such that every vertex of D is either in S or dominates a vertex in S. If every induced subdigraph of D has a kernel, then D is called kernel-perfect. According to Richardson, if a digraph does not contain a directed cycle of odd length then it is kernel-perfect. Here we study the kernel-perfection through use of the push...
This paper proposes a new approach that uses social networks and common sense deduction rules to adapt the description tags of the photos for the current viewer. We exploit social graphs to enrich the tags associated to the concerned persons in the photo by following the different links between people (i.e. viewer and captured people in the photos). The main contributions of our work are: (i) addition...
Natural Intelligence is based not only on conscious procedural and declarative knowledge, but also on knowledge that is inferred from observing the actions of others. This knowledge is tacit, in that the process of its acquisition remains unspecified. However, tacit knowledge is an accepted guide of behavior, especially in unfamiliar contexts. In situations where knowledge is lacking, animals act...
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