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Guest Editors Introduction: Special Section on Recent Advances in Network Security Management

As the backbone of communications amongst objects, humans, companies, and administrations, the Internet has become a great integration platform capable of efficiently interconnecting billions of entities, from RFID chips to data centers. This platform provides access to multiple hardware and virtualized resources (servers, networking, storage, applications, connected objects) coming from cloud computing and Internet-of-Things (IoT) infrastructures. From these resources that may be hosted and distributed amongst different providers and tenants, the building and operation of complex and value-added networked systems is enabled.

A novel relevance-based information interaction model for community detection in complex networks

Identification of patronized communities in a complex network structure is an essential aspect of network analysis. These identifications really become challenging when we come across the tasks of dealing with such large-scale community networks when their vertices and edges span over billions of entities like social media networks, news networks, welfare associations, NGO networks etc., to look for a pattern match. Although several incentive approaches have been developed and deployed to ease the process, still there is no valuable alleviation in the severity of the concern; for instance, some of the methods just consider the local information of a node in a network, whereas some of them consider only global information, and some more methods need end-users to provide advanced knowledge about the community structure. To cope with these research issues, an effective community detection approach is required to feasibly alleviate the computational overhead of dealing with the network. Therefore, we propose a novel Relevance-based Information Interaction Model (RIIM) to identify communities in complex networks based on local as well as global topological aspects without providing prior community knowledge and parameter configuration. In order to evaluate the effectiveness of RIIM, we conducted extensive experiments on real and synthetic networks, and the generated performance demonstrates that the proposed approach outperformed the state-of-the-art techniques by effectively identifying the corresponding communities in complex networks.

Guest Editors’ Introduction: Special Issue on Latest Developments for Security Management of Networks and Services

As the backbone of communications amongst objects, humans, companies, and administrations, the Internet has become a great integration platform capable of efficiently interconnecting billions of entities, from RFID chips to data centers. This platform provides access to multiple hardware and virtualized resources (servers, networking, storage, applications, connected objects) coming from cloud computing and Internet-of-Things (IoT) infrastructures. From these resources that may be hosted and distributed amongst different providers and tenants, the building and operation of complex and value-added networked systems is enabled.

Guest Editorial: Special Section on Cybersecurity Techniques for Managing Networked Systems

As the backbone of communications amongst objects, humans, companies, and administrations, the Internet has become a great integration platform capable of efficiently interconnecting billions of entities, from RFID chips to data centers. This platform provides access to multiple hardware and virtualized resources (servers, networking, storage, applications, connected objects) coming from cloud computing and Internet-of-Things (IoT) infrastructures. From these resources that may be hosted and distributed amongst different providers and tenants, the building and operation of complex and value-added networked systems is enabled.

Investigation of Internet of Things Handover Process for Information Centric Networking and Proxy Mobile Internet Protocol

IoT (Internet of Things) technically connects billions of entities to the Internet. The IoT is divided between the technology and the service itself. As a result, great efforts are needed to join data from many contexts and services. This reason has motivated proposals to develop solutions that can overcome existing issues of limitations for mobility, security, reliability and scalability of IoT. These billions of devices are interconnected to each other either using unicast, multicast or broadcast communications, and mixture of static and mobile communications. This paper aims to investigate the parameters of mobility performance in handover process for mobile multicast IoT environment. Investigation is done quantitatively by evaluating the parameters of handover process for IoT in two networking protocols that are possible to support acceptable mobility performance for IoT. The protocols are ICN (Information Centric Networking) and Proxy Mobile Internet Protocol. The evaluation parameters include packet loss and service recovery time. The metrics are extracted from the handover process flow for each network protocol topology. The service recovery time parameter is assumed as the time duration for each message to travel from sender to receiver, while packet loss parameter depends on the packet arrival rate and service recovery time. The results show that the ICN performs better than Proxy Mobile Internet Protocol.

Querying Web-Scale Knowledge Graphs Through Effective Pruning of Search Space

Web-scale knowledge graphs containing billions of entities are common nowadays. Querying these graphs can be modeled as a subgraph matching problem. Since knowledge graphs are incomplete and noisy in nature, it is important to discover answers matching exactly as well as answers similar to queries. Existing graph matching algorithms usually use graph indices to accelerate query processing. For billion-node graphs, it may be infeasible to build the graph indices due to the amount of work and the memory/ storage required. In this paper, we propose an efficient algorithm for finding the best k answers for a given query without precomputing graph indices. An answer's quality is measured by a matching score that is computed online. To accelerate query processing, we propose a novel technique for bounding the matching scores during the computation. By using bounds, the low quality answers can be efficiently pruned. The bounding technique can be implemented in a distributed environment, allowing our approach to efficiently query web-scale knowledge graphs. We evaluate the effectiveness and the efficiency of our approach on real-world datasets. The result shows that our bounding technique can reduce the running time up to two orders of magnitude comparing to an approach without using bounds.

A bootstrapping approach to entity linkage on the Semantic Web

In the Big Data era, ever-increasing RDF data have reached a scale in billions of entities and brought challenges to the problem of entity linkage on the Semantic Web. Although millions of entities, typically denoted by URIs, have been explicitly linked with owl:sameAs, potentially coreferent ones are still numerous. Existing automatic approaches address this problem mainly from two perspectives: one is via equivalence reasoning, which infers semantically coreferent entities but probably misses many potentials; the other is by similarity computation between property-values of entities, which is not always accurate and do not scale well. In this paper, we introduce a bootstrapping approach by leveraging these two kinds of methods for entity linkage. Given an entity, our approach first infers a set of semantically coreferent entities. Then, it iteratively expands this entity set using discriminative property-value pairs. The discriminability is learned with a statistical measure, which does not only identify important property-values in the entity set, but also takes matched properties into account. Frequent property combinations are also mined to improve linkage accuracy. We develop an online entity linkage search engine, and show its superior precision and recall by comparing with representative approaches on a large-scale and two benchmark datasets.

A Bootstrapping Approach to Entity Linkage on the Semantic Web

In the Big Data era, ever-increasing RDF data have reached a scale in billions of entities and brought challenges to the problem of entity linkage on the Semantic Web. Although millions of entities, typically denoted by URIs, have been explicitly linked with owl:sameAs, potentially coreferent ones are still numerous. Existing automatic approaches address this problem mainly from two perspectives: one is via equivalence reasoning, which infers semantically coreferent entities but probably misses many potentials; the other is by similarity computation between property-values of entities, which is not always accurate and do not scale well. In this paper, we introduce a bootstrapping approach by leveraging these two kinds of methods for entity linkage. Given an entity, our approach firstly infers a set of semantically coreferent entities. Then, it iteratively expands this entity set using discriminative property value pairs. The discriminability is learned with a statistical measure, which does not only identify important property-values in the entity set, but also takes matched properties into account. Frequent property combinations are also mined to improve linkage accuracy. We develop an online entity linkage search engine, and show its superior precision and recall by comparing with representative approaches on a large-scale and two benchmark datasets.

Mining and generating large-scaled social networks via MapReduce

The computational efficiency is usually a concern when dealing with large-scale social network mining tasks containing billions of entities. Cloud computing is widely regarded as a feasible solution to this problem. In this work, we present an open-source graph mining library called the MapReduce Graph Mining Framework (MGMF) to be a robust and efficient MapReduce-based graph mining tool. We start from dividing graph mining algorithms into several categories and design a MapReduce framework for algorithms in each category. The experimental results show that MGMF is 3–20 times more efficient than PEGASUS, a state-of-the-art library for graph mining on MapReduce. Moreover, it provides broader coverage of a variety of graph mining algorithms. Furthermore, we designed a model to generate large-scale social networks capturing the power-law degree distribution property by parallelizing the mechanism of preferential attachment so that it is possible to produce billion-sized scale-free network in minutes. Our implemented open-source library can be downloaded from http://mslab.csie.ntu.edu.tw/~noahsark/MGMF/.