Source localization in signed networks with effective distance

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

Constructing refined null models for statistical analysis of signed networks

The establishment of effective null models can provide reference networks to accurately describe statistical properties of real-life signed networks.At present,two classical null models of signed networks(i.e.,sign and full-edge randomized models)shuffle both positive and negative topologies at the same time,so it is difficult to distinguish the effect on network topology of positive edges,negative edges,and the correlation between them.In this study,we construct three re-fined edge-randomized null models by only randomizing link relationships without changing positive and negative degree distributions.The results of nontrivial statistical indicators of signed networks,such as average degree connectivity and clustering coefficient,show that the position of positive edges has a stronger effect on positive-edge topology,while the signs of negative edges have a greater influence on negative-edge topology.For some specific statistics(e.g.,embeddedness),the results indicate that the proposed null models can more accurately describe real-life networks compared with the two existing ones,which can be selected to facilitate a better understanding of complex structures,functions,and dynamical behaviors on signed networks.

Disagreement and Antagonism in Signed Networks: A Survey

Signed networks refer to a class of network systems including not only cooperative but also antagonistic interactions among nodes.Due to the existence of antagonistic interactions in signed networks,the agreement of nodes may not be established,instead of which disagreement behaviors generally emerge.This paper reviews several different disagreement behaviors in signed networks under the single-integrator linear dynamics,where two classes of topologies,namely,the static topology and the dynamic topology,are considered.For the static signed networks with the adjacency weights as(time-varying)scalars,we investigate the convergence behaviors and the fluctuation behaviors with respect to fixed topologies and switching topologies,respectively,and give some brief introductions on the disagreement behaviors of general time-varying signed networks.Correspondingly,several classes of behavior analysis approaches are also provided.For the dynamic signed networks with the adjacency weights as transfer functions or linear time-invariant systems,we show the specific descriptions and characteristics of them such that the disagreement behaviors can be obtained by resorting to the derived static signed graphs.Furthermore,we give their applications to the behavior analysis of static signed networks in the presence of high-order dynamics or communication delays.

Community detection in signed networks based on discrete-time model

Community detection in signed networks has been studied widely in recent years. In this paper, a discrete difference equation is proposed to imitate the consistently changing phases of the nodes. During the interaction, each node will update its phase based on the difference equation. Each node has many different nodes connected with it, and these neighbors have different influences on it. The similarity between two nodes is applied to describe the influences between them. Nodes with high positive similarities will get together and nodes with negative similarities will be far away from each other.Communities are detected ultimately when the phases of the nodes are stable. Experiments on real world and synthetic signed networks show the efficiency of detection performance. Moreover, the presented method gains better detection performance than two existing good algorithms.

The Dynamics of Two-State Public Opinion Propagation On Signed Networks

The formation of public opinion on the network is a hot issue in the field of complex network research, and some classical dynamic models are used to solve this problem. The signed network is a particular form of the complex network, which can adequately describe the amicable and hostile relationships in complex real-world systems. However, the methods for studying the dynamic process of public opinion propagation on signed networks still require to be further discussed. In this paper, the authors pay attention to the influence of negative edges in order to design a two-state public opinion propagation mechanism suitable for signed networks. The authors first set the interaction rules between nodes and the transition rules of node states and then apply the model to synthetic and real-world signed networks. The simulation results show that there is a critical value of the negative edge ratio.When the negative edge ratio exceeds this critical value, the evolutionary result of public opinion will change from a consistent state to a split state. This conclusion is also consistent with the distribution result of opinions within communities in the signed network. Besides, the research on the network structural balance shows that the model makes the network evolve in a more balanced direction.

Bipartite consensus of higher-order multi-agent systems based on event-triggered control and signed network

Consensus of higher-order multi-agent systems based on event-triggered control and signed network is studied in this paper,where signed multi-agent systems control protocol takes hostile relations into account between different agents.Event-triggered protocol for the second-order signed multi-agent systems is proposed.Similar results are obtained for high-order signed multiagent systems.In addition,for the two control strategies proposed in this paper,it is proved that Zeno behaviours do not exist.Finally,simulation examples are presented to verify the correctness of the results.

A Heuristic Clustering Algorithm for Mining Communities in Signed Networks

Signed network is an important kind of complex network, which includes both positive relations and negative relations. Communities of a signed network are defined as the groups of vertices, within which positive relations are dense and between which negative relations are also dense. Being able to identify communities of signed networks is helpful for analysis of such networks. Hitherto many algorithms for detecting network communities have been developed. However, most of them are designed exclusively for the networks including only positive relations and are not suitable for signed networks. So the problem of mining communities of signed networks quickly and correctly has not been solved satisfactorily. In this paper, we propose a heuristic algorithm to address this issue. Compared with major existing methods, our approach has three distinct features. First, it is very fast with a roughly linear time with respect to network size. Second, it exhibits a good clustering capability and especially can work well with complex networks without well-defined community structures. Finally, it is insensitive to its built-in parameters and requires no prior knowledge.

Controllability of General Linear Discrete Multi-Agent Systems with Directed and Weighted Signed Network

This paper investigates the controllability of general linear discrete-time multi-agent systems with directed and weighted signed networks by using graphic and algebraic methods.The nondelay and delay cases are considered respectively.For the case of no time delay,the upper bound condition of the controllable subspace is given by using the equitable partition method,and the influence of coefficient matrix selection of individual dynamics is illustrated.For the case of single delay and multiple delays,the equitable partition method is extended to deal with time-delay systems,and some conclusions are obtained.In particular,some simplified algebraic criteria for controllability of systems with time delay are obtained by using augmented system method and traditional algebraic controllability criteria.

A New Method Based on Evolutionary Algorithm for Symbolic Network Weak Unbalance

The symbolic network adds the emotional information of the relationship,that is,the“+”and“-”information of the edge,which greatly enhances the modeling ability and has wide application in many fields.Weak unbalance is an important indicator to measure the network tension.This paper starts from the weak structural equilibrium theorem,and integrates the work of predecessors,and proposes the weak unbalanced algorithm EAWSB based on evolutionary algorithm.Experiments on the large symbolic networks Epinions,Slashdot and WikiElections show the effectiveness and efficiency of the proposed method.In EAWSB,this paper proposes a compression-based indirect representation method,which effectively reduces the size of the genotype space,thus making the algorithm search more complete and easier to get better solutions.

ZTE Signs Network Global Framework Agreement with Vodafone

ZTE Corporation (ZTE), a leading global provider of telecommunications equipment and network solutions, has signed a network equipment Global Framework Agreement (GFA) with Vodafone on spanning ZTE’s complete telecoms infrastructure equipment portfolio.

Structure Balance and Opinions Dynamic in Signed Social Network

In this paper,the authors consider both the nodes’opinions dynamics and signed network edges’evolution.Simulated Annealing Algorithm is applied for searching the minimal global energy function,and bounded confidence model is used for nodes’opinions updating.The authors find that the local and global level of balance of signed network is consistent with collective opinions 2-polarization.This property is explainable in terms of the structure balance of the sign distributions on the nodes and edges.The level of balance and the final opinions polarization pattern are achieved depends on the initial density of signed network,and the percentage of initial positive edges.Numerical simulations of the proposed model display a rich and intuitive behavior of the opinions polarization processes.In particular,the authors show that opinions persistent fluctuations is consistent with minimal global the energy function.This work verify that signed social networks are indeed limited balanced,could be used to explain ubiquitous binary polarization phenomenon of real world.