2,203 research outputs found
Seed selection for information cascade in multilayer networks
Information spreading is an interesting field in the domain of online social
media. In this work, we are investigating how well different seed selection
strategies affect the spreading processes simulated using independent cascade
model on eighteen multilayer social networks. Fifteen networks are built based
on the user interaction data extracted from Facebook public pages and tree of
them are multilayer networks downloaded from public repository (two of them
being Twitter networks). The results indicate that various state of the art
seed selection strategies for single-layer networks like K-Shell or VoteRank do
not perform so well on multilayer networks and are outperformed by Degree
Centrality
Influence maximization in multilayer networks based on adaptive coupling degree
Influence Maximization(IM) aims to identify highly influential nodes to
maximize influence spread in a network. Previous research on the IM problem has
mainly concentrated on single-layer networks, disregarding the comprehension of
the coupling structure that is inherent in multilayer networks. To solve the IM
problem in multilayer networks, we first propose an independent cascade model
(MIC) in a multilayer network where propagation occurs simultaneously across
different layers. Consequently, a heuristic algorithm, i.e., Adaptive Coupling
Degree (ACD), which selects seed nodes with high spread influence and a low
degree of overlap of influence, is proposed to identify seed nodes for IM in a
multilayer network. By conducting experiments based on MIC, we have
demonstrated that our proposed method is superior to the baselines in terms of
influence spread and time cost in 6 synthetic and 4 real-world multilayer
networks
Effective Influence Spreading in Temporal Networks with Sequential Seeding
The spread of influence in networks is a topic of great importance in many
application areas. For instance, one would like to maximise the coverage,
limiting the budget for marketing campaign initialisation and use the potential
of social influence. To tackle this and similar challenges, more than a decade
ago, researchers started to investigate the influence maximisation problem. The
challenge is to find the best set of initially activated seed nodes in order to
maximise the influence spread in networks. In typical approach we will activate
all seeds in single stage, at the beginning of the process, while in this work
we introduce and evaluate a new approach for seeds activation in temporal
networks based on sequential seeding. Instead of activating all nodes at the
same time, this method distributes the activations of seeds, leading to higher
ranges of influence spread. The results of experiments performed using real and
randomised networks demonstrate that the proposed method outperforms single
stage seeding in 71% of cases by nearly 6% on average. Knowing that temporal
networks are an adequate choice for modelling dynamic processes, the results of
this work can be interpreted as encouraging to apply temporal sequential
seeding for real world cases, especially knowing that more sophisticated seed
selection strategies can be implemented by using the seed activation strategy
introduced in this work.Comment: 11 pages, 10 figures, reproductory code availabl
Dynamical Systems on Networks: A Tutorial
We give a tutorial for the study of dynamical systems on networks. We focus
especially on "simple" situations that are tractable analytically, because they
can be very insightful and provide useful springboards for the study of more
complicated scenarios. We briefly motivate why examining dynamical systems on
networks is interesting and important, and we then give several fascinating
examples and discuss some theoretical results. We also briefly discuss
dynamical systems on dynamical (i.e., time-dependent) networks, overview
software implementations, and give an outlook on the field.Comment: 39 pages, 1 figure, submitted, more examples and discussion than
original version, some reorganization and also more pointers to interesting
direction
A Survey of Social Network Analysis Techniques and their Applications to Socially Aware Networking
Socially aware networking is an emerging research field that aims to improve the current networking technologies and realize novel network services by applying social network analysis (SNA) techniques. Conducting socially aware networking studies requires knowledge of both SNA and communication networking, but it is not easy for communication networking researchers who are unfamiliar with SNA to obtain comprehensive knowledge of SNA due to its interdisciplinary nature. This paper therefore aims to fill the knowledge gap for networking researchers who are interested in socially aware networking but are not familiar with SNA. This paper surveys three types of important SNA techniques for socially aware networking: identification of influential nodes, link prediction, and community detection. Then, this paper introduces how SNA techniques are used in socially aware networking and discusses research trends in socially aware networking
Influence Spread in Two-Layer Interdependent Networks: Designed Single-Layer or Random Two-Layer Initial Spreaders?
Influence spread in multi-layer interdependent networks (M-IDN) has been studied in the last few years; however, prior works mostly focused on the spread that is initiated in a single layer of an M-IDN. In real world scenarios, influence spread can happen concurrently among many or all components making up the topology of an M-IDN. This paper investigates the effectiveness of different influence spread strategies in M-IDNs by providing a comprehensive analysis of the time evolution of influence propagation given different initial spreader strategies. For this study we consider a two-layer interdependent network and a general probabilistic threshold influence spread model to evaluate the evolution of influence spread over time. For a given coupling scenario, we tested multiple interdependent topologies, composed of layers A and B, against four cases of initial spreader selection: (1) random initial spreaders in A, (2) random initial spreaders in both A and B, (3) targeted initial spreaders using degree centrality in A, and (4) targeted initial spreaders using degree centrality in both A and B. Our results indicate that the effectiveness of influence spread highly depends on network topologies, the way they are coupled, and our knowledge of the network structure — thus an initial spread starting in only A can be as effective as initial spread starting in both A and B concurrently. Similarly, random initial spread in multiple layers of an interdependent system can be more severe than a comparable initial spread in a single layer. Our results can be easily extended to different types of event propagation in multi-layer interdependent networks such as information/misinformation propagation in online social networks, disease propagation in offline social networks, and failure/attack propagation in cyber-physical systems
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