We propose a community structure‐based approach that does not require community labels of nodes, for network immunization. Social networks have been widely used as daily communication infrastructures these days. However, fast spreading of information over networks may have downsides such as computer viruses or epidemics of diseases. Because contamination is propagated among subgraphs (communities) along links in a network, use of community structure of the network would be effective for network immunization. However, despite various research efforts, it is still difficult to identify ground‐truth community labels of nodes in a network. Because communities are often interwoven through intermediate nodes, we propose to identify such nodes based on the community structure of a network without requiring community labels. By regarding the community structure in terms of nodes, we construct a vector representation of nodes based on a quality measure of communities. The distribution of the constructed vectors is used for immunizing intermediate nodes among communities, through the hybrid use of the norm and the relation in the vector representation. Experiments are conducted over both synthetic and real‐world networks, and our approach is compared with other network centrality‐based approaches. The results are encouraging and indicate that it is worth pursuing this path.

This paper proposes a graph‐based approach for semisupervised clustering based on pairwise relations among instances. In our approach, the entire data set is represented as an edge‐weighted graph by mapping each data element (instance) as a vertex and connecting the instances by edges with their similarities. In order to reflect pairwise constraints on the clustering process, the graph is modified by contraction as it is known from general graph theory and the graph Laplacian in spectral graph theory. The graph representation enables us to deal with pairwise constraints as well as pairwise similarities over the same unified representation. By exploiting the constraints as well as similarities among instances, the entire data set is projected onto a subspace via the modified graph, and data clustering is conducted over the projected representation. The proposed approach is evaluated over several real‐world data sets. The results are encouraging and show that it is worthwhile to pursue the proposed approach.

Business process management, service-oriented architectures and software back-engineering heavily rely on the fundamental processes of mining of processes and web service business protocols from log files. Model extraction and mining aim at the (re)discovery of the behavior of a running model implementation using solely its interaction and activity traces, and no a priori information on the target model. This paper presents an approach for correlating messages and extracting the business protocol of a web service in the realistic scenario in which correlation information is entirely absent from interaction and activity logs. Correlation is achieved through deterministic computations that result in an extremely efficient method whose extensive experiments have shown its solid reliability, robustness when dealing with complex structures, and very high performance and scalability. This approach and the underlying algorithms extend what is actually possible to achieve in the web service business protocol mining domain using incomplete and noisy data logs, and opens new horizons in back-engineering of web services. The theoretical and experimental results clearly show the leap forward achieved herein.

When class distribution changes, some pieces of knowledge previously acquired become worthless, and the existence of such knowledge may hinder acquisition of new knowledge. The paper proposes an adaptive ripple down rules (RDR) method based on the minimum description length principle aiming at knowledge acquisition in a dynamically changing environment. To cope with the change of class distribution, knowledge deletion is carried out as well as knowledge acquisition so that useless knowledge is properly discarded. To cope with the change of the source of knowledge, RDR knowledge based systems can be constructed adaptively by acquiring knowledge from both domain experts and data. By incorporating inductive learning methods, knowledge acquisition can be carried out even when only either data or experts are available by switching the source of knowledge from domain experts to data and vice versa at any time of knowledge acquisition. Since experts need not be available all the time, it contributes to reducing the cost of personnel expenses. Experiments were conducted by simulating the change of the source of knowledge and the change of class distribution using the datasets in UCI repository. The results are encouraging.

This paper proposes an evolutionary approach for discovering difference in the usage of words to facilitate collaboration among people. In general, different people seem to have different ways of conception and thus can have different concepts even on the same thing. When people try to communicate their concepts with words, such difference in the meaning and usage can lead to misunderstanding in communication, which can hinder their collaboration. In our approach each granule of knowledge in classification from users is structured into a decision tree so that difference in the usage of words can be discovered as difference in the structure of decision trees. By treating each granule of classification knowledge (i.e., decision tree) as an individual in Genetic Algorithm (GA), evolution is carried out with respect to both classification efficiency of each individual and diversity as a population so that the granule for classification is gradually evolved with diverse structure. Experiments were carried out on motor diagnosis cases with artificially encoded difference in the usage of words and the result shows the effectiveness of the proposed evolutionary approach.

Hypermedia systems have become increasingly popular as tools for user-driven learning and information retrieval. This paper describes a dynamic linkage method that allows adaptive modification of links in hypermedia dependent on users, with the aim of providing satisfactory navigation for each user. A design support tool for constructing network data in accordance with the method is also presented, and a prototype system and an associated design support tool are evaluated.