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The authors will review the main concepts of graphs, present the implemented algorithm, as well as explain the different techniques applied to the graph, to achieve an efficient execution of the algorithm, both in terms of the use of multiple cores that the authors have available today, and the use of massive data parallelism through the parallelization of the algorithm, bringing the graph closer to the execution through CUDA on GPUs.

In this work, the authors approach the graphs isomorphism problem, approaching this problem from a point of view very little worked during all this time, the application of parallelism and the high-performance computing (HPC) techniques to the detection of isomorphism between graphs.

Results obtained give compelling reasons to ensure that more in-depth studies on the HPC techniques should be applied in these fields, since gains of up to 722x speedup are achieved in the most favorable scenarios, maintaining an average performance speedup of 454x.

The paper is new and original.

The purpose of this paper is to provide a detailed discussion for different types of graph queries and a different mechanism for indexing and querying graph databases.

The paper reviews the existing approaches and techniques for indexing and querying graph databases. For each approach, the strengths and weaknesses are discussed with particular emphasis on the target application domain. Based on an analysis of the state‐of‐the‐art of research literature, the paper provides insights for future research directions and untouched challenging research aspects.

Several graph indexing and querying techniques have been proposed in the literature. However, there is still a clear room for improvement and further research issues in that domain.

The paper identifies the advantages and disadvantages of the different graph indexing mechanisms and their suitability for different practical applications. The paper provides some guidelines and recommendations which are useful for future research in the area of graph databases.

The paper has practical implications for social networks, protein networks, chemical compounds, multimedia database, and semantic web.

The paper contributes to the implementation of an efficient indexing and querying mechanism for graph databases in different application domains.

The purpose of this paper is to propose a three-dimensional (3D) assembly model retrieval method based on assembling semantic information to address semantic mismatches, poor accuracy and low efficiency in existing 3D assembly model retrieval methods.

The paper proposes an assembly model retrieval method. First, assembly information retrieval is performed, and 3D models that conform to the design intention of the assembly are found by retrieving the code. On this basis, because there are conjugate subgraphs between attributed adjacency graphs (AAG) that have an assembly relationship, the assembly model geometric retrieval is translated into a problem of finding AAGs with a conjugate subgraph. Finally, the frequent subgraph mining method is used to retrieve AAGs with conjugate subgraphs.

The method improved the efficiency and accuracy of assembly model retrieval.

The examples illustrate the specific retrieval process and verify the feasibility and reasonability of the assembly model retrieval method in practical applications.

The assembly model retrieval method in the paper is an original method. Compared with other methods, good results were obtained.

Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.

Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.

Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.

This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

Virtual assembly process plays an important role in assembly design of complex product and is typically time- and resource-intensive. This paper aims to investigate a dynamic assembly simplification approach in order to demonstrate and interact with virtual assembly process of complex product in real time.

The proposed approach regards the virtual assembly process of complex product as an incremental growth process of dynamic assembly. During the growth process, the current-assembled-state assembly model is simplified with appearance preserved by detecting and removing its invisible features, and the to-be-assembled components are simplified with assembly features preserved using conjugated subgraphs matching method based on an improved subgraph isomorphism algorithm.

The dynamic assembly simplification approach is applied successfully to reduce the complexity of computer aided design models during the virtual assembly process and it is proved by several cases.

A new assembly features definition is proposed based on the notion of “conjugation” to assist the assembly features recognition, which is a main step of the dynamic assembly simplification and has been translated into conjugated subgraphs matching problem. And an improved subgraph isomorphism algorithm is presented to address this problem.

A simultaneous solution to the localization and mapping problem of a graph‐like environment by a swarm of robots requires solutions to task coordination and map merging. The purpose of this paper is to examine the performance of two different map‐merging strategies.

Building a representation of the environment is a key problem in robotics where the problem is known as simultaneous localization and mapping (SLAM). When large groups of robots operate within the environment, the SLAM problem becomes complicated by issues related to coordination of the elements of the swarm and integration of the environmental representations obtained by individual swarm elements. This paper considers these issues within the formalism of a group of simulated robots operating within a graph‐like environment. Starting at a common node, the swarm partitions the unknown edges of the known graph and explores the graph for a pre‐arranged period. The swarm elements then meet at a particular time and location to integrate their partial world models. This process is repeated until the entire world has been mapped. A correctness proof of the algorithm is presented, and different coordination strategies are compared via simulation.

The paper demonstrates that a swarm of identical robots, each equipped with its own marker, and capable of simple sensing and action abilities, can explore and map an unknown graph‐like environment. Moreover, experimental results show that exploration with multiple robots can provide an improvement in exploration effort over a single robot and that this improvement does not scale linearly with the size of the swarm.

The paper represents efforts toward exploration and mapping in a graph‐like world with robot swarms. The paper suggests several extensions and variations including the development of adaptive partitioning and rendezvous schedule strategies to further improve both overall swarm efficiency and individual robot utilization during exploration.

The novelty associated with this paper is the formal extension of the single robot graph‐like exploration of Dudek et al. to robot swarms. The paper here examines fundamental limits to multiple robot SLAM and does this within a topological framework. Results obtained within this topological formalism can be readily transferred to the more traditional metric representation.

The arguments in this chapter address all three of the questions motivating this volume on network strategy. First, they focus on the issue of network evolution and show how networks can emerge and change over time. Second, the chapter tackles the issue of endogeneity and shows that, under certain conditions, some structural advantages do precede rather than follow network positions. Networks evolve over trajectories and the trajectories matter. Third, the arguments respond to the core question of network entrepreneurship: does the awareness of structural advantages available to network positions inspire managers, acting on behalf of organizations, to seek these advantages? In responding, this chapter challenges the idea that filling structural holes necessarily confers advantages on the actors filling them. It follows that the advantages of bridging are dependent not only on the network structure, when decisions regarding tie formation or deletion are made, but also on the costs of forming and maintaining ties relative to the benefits obtained from doing so.

This paper aims to develop a framework for object trust evaluation and related object trust principles to facilitate knowledge management in a virtual organization. It proposes systematic methods to quantify the trust of an object and defines the concept of object trust management. The study aims to expand the domain of subject trust to object trust evaluation in terms of whether an object is correct and accurate in expressing a topic or issue and whether the object is secure and safe to execute (in the case of an executable program). By providing theoretical and empirical insights about object trust composition and combination, this research facilitates better knowledge identification, creation, evaluation, and distribution.

This paper presents two object trust principles – trust composition and trust combination. These principles provide formal methodologies and guidelines to assess whether an object has the required level of quality and security features (hence it is trustworthy). The paper uses a component‐based approach to evaluate the quality and security of an object. Formal approaches and algorithms have been developed to assess the trustworthiness of an object in different cases.

The paper provides qualitative and quantitative analysis about how object trust can be composed and combined. Novel mechanisms have been developed to help users evaluate the quality and security features of available objects.

This effort fulfills an identified need to address the challenging issue of evaluating the trustworthiness of an object (e.g. a software program, a file, or other type of knowledge element) in a loosely‐coupled system such as a virtual organization. It is the first of its kind to formally define object trust management and study object trust evaluation.

Current approaches to text retrieval based on indexing by words or index terms and on retrieving by specifying a Boolean combination of keywords are well known, as are their limitations. Statistical approaches to retrieval, as exemplified in commercial products like STATUS/IQ and Personal Librarian, are slightly better but still have their own weaknesses. Approaches to the indexing and retrieval of text based on techniques of automatic natural language processing (NLP) may soon start to realise their undoubted potential in terms of improving the quality and effectiveness of information retrieval. In this article we will explore what that potential is. We will divide information retrieval functionality into conceptual and traditional information retrieval and we will examine some of the current attempts at using various NLP techniques in both the indexing and retrieval operations.

The paper's purpose is to propose a localization algorithm for topological maps constituted by nodes and edges in a graph form. The focus is to develop a robust localization algorithm that works well even under various dynamic noises.

For robust localization, the authors propose an algorithm which utilizes all available data such as node information, sensor measurements at the current time step (which are used in previous algorithms) and edge information, and sensor measurements at previous time steps (which have not been considered in other papers). Also, the algorithm estimates a robot's location in a multi‐modal manner which increases its robustness.

Findings show that the proposed algorithm works well in topological maps with various dynamics which are induced by the moving objects in the map and measurement noises from cheap sensors.

Unlike previous approaches, the proposed algorithm has three key features: usage of edge data, inclusion of history information, and a multi‐modal based approach. By virtue of these features, the paper develops an algorithm that enables robust localization performance.