Network theory

About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.

A degree centrality in multi-layered social network

Abstract: Multi-layered social networks reflect complex relationships existing in modern interconnected IT systems. In such a network each pair of nodes may be linked by many edges that correspond to different communication or collaboration user activities. Multi-layered degree centrality for multi-layered social networks is presented in the paper. Experimental studies were carried out on data collected from the real Web 2.0 site. The multi-layered social network extracted from this data consists of ten distinct layers and the network analysis was performed for different degree centralities measures.

Message Passing Optimization of Harmonic Influence Centrality

Abstract: This paper proposes a new measure of node centrality in social networks, the Harmonic Influence Centrality (HIC), which emerges naturally in the study of social influence over networks. Using an intuitive analogy between social and electrical networks, we introduce a distributed message passing algorithm to compute the HIC of each node. Although its design is based on theoretical results which assume the network to have no cycle, the algorithm can also be successfully applied on general graphs.

Defining and delivering resilient ecological networks: nature conservation in England

Abstract: 1. Planning for nature conservation has increasingly emphasised the concepts of resilience and spatial networks. Although the importance of networks of habitat for individual species is clear, their importance for long-term ecological resilience and multi-species conservation strategies is less well established. 2. Referencing spatial network theory, we describe the conceptual basis for defining and assessing a network of wildlife areas that supports the resilience of species to multiple forms of perturbations and pressures. We explore actions that could enhance network resilience at a range of scales, based on ecological principles, with reference to four well-established strategies for intervention in a spatial network (Better, Bigger, More and Joined) from the influential Making Space for Nature report by Lawton et al. (2010). 3. Building existing theory into useable and scalable approaches applicable to large numbers of species is challenging but tractable. We illustrate the policy context, describe the elements of a long-term adaptive management plan and provide example actions, metrics and targets for early implementation using England as a case study, where there is an opportunity to include large-scale ecological planning in a newly launched 25-year environment plan. 4. Policy Implications: The scientific principles to place resilience and network theory at the heart of large-scale and long-term environmental planning are established and ready to implement in practice. Delivering a resilient network to support nature recovery is achievable, and can be integrated with ongoing conservation actions. England’s 25 Year Environment Plan provides the ideal testbed.

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Abstract: Centrality is one of the most studied concepts in network analysis. Despite an abundance of methods for measuring centrality in social networks has been proposed, each approach exclusively characterizes limited parts of what it implies for an actor to be “vital” to the network. In this paper, a novel mechanism is proposed to quantitatively measure centrality using the re-defined entropy centrality model, which is based on decompositions of a graph into subgraphs and analysis on the entropy of neighbor nodes. By design, the re-defined entropy centrality which describes associations among node pairs and captures the process of influence propagation can be interpreted explained as a measure of actor potential for communication activity. We evaluate the efficiency of the proposed model by using four real-world datasets with varied sizes and densities and three artificial networks constructed by models including Barabasi-Albert, Erdos-Renyi and Watts-Stroggatz. The four datasets are Zachary’s karate club, USAir97, Collaboration network and Email network URV respectively. Extensive experimental results prove the effectiveness of the proposed method.

Cheap, easy, and massively effective viral marketing in social networks: truth or fiction?

Abstract: Online social networks (OSNs) have become one of the most effective channels for marketing and advertising. Since users are often influenced by their friends, "word-of-mouth" exchanges so-called viral marketing in social networks can be used to increases product adoption or widely spread content over the network. The common perception of viral marketing about being cheap, easy, and massively effective makes it an ideal replacement of traditional advertising. However, recent studies have revealed that the propagation often fades quickly within only few hops from the sources, counteracting the assumption on the self-perpetuating of influence considered in literature. With only limited influence propagation, is massively reaching customers via viral marketing still affordable? How to economically spend more resources to increase the spreading speed?We investigate the cost-effective massive viral marketing problem, taking into the consideration the limited influence propagation. Both analytical analysis based on power-law network theory and numerical analysis demonstrate that the viral marketing might involve costly seeding. To minimize the seeding cost, we provide mathematical programming to find optimal seeding for medium-size networks and propose VirAds, an efficient algorithm, to tackle the problem on large-scale networks. VirAds guarantees a relative error bound of O(1) from the optimal solutions in power-law networks and outperforms the greedy heuristics which realizes on the degree centrality. Moreover, we also show that, in general, approximating the optimal seeding within a ratio better than O(log n) is unlikely possible.