19 research outputs found
Improving the accuracy of the k-shell method by removing redundant links-from a perspective of spreading dynamics
Recent study shows that the accuracy of the k-shell method in determining
node coreness in a spreading process is largely impacted due to the existence
of core-like group, which has a large k-shell index but a low spreading
efficiency. Based on analysis of the structure of core-like groups in
real-world networks, we discover that nodes in the core-like group are mutually
densely connected with very few out-leaving links from the group. By defining a
measure of diffusion importance for each edge based on the number of
out-leaving links of its both ends, we are able to identify redundant links in
the spreading process, which have a relatively low diffusion importance but
lead to form the locally densely connected core-like group. After filtering out
the redundant links and applying the k-shell method to the residual network, we
obtain a renewed coreness for each node which is a more accurate index to
indicate its location importance and spreading influence in the original
network. Moreover, we find that the performance of the ranking algorithms based
on the renewed coreness are also greatly enhanced. Our findings help to more
accurately decompose the network core structure and identify influential nodes
in spreading processes.Comment: 18 pages, 14 figure
Identifying influential patents in citation networks using enhanced VoteRank centrality
This study proposes the usage of a method called VoteRank, created by Zhang
et al. (2016), to identify influential nodes on patent citation networks. In
addition, it proposes enhanced VoteRank algorithms, extending the Zhang et al.
work. These novel algorithms comprise a reduction on the voting ability of the
nodes affected by a chosen spreader if the nodes are distant from the spreader.
One method uses a reduction factor that is linear regarding the distance from
the spreader, which we called VoteRank-LRed. The other method uses a reduction
factor that is exponential concerning the distance from the spreader, which we
called VoteRank-XRed. By applying the methods to a citation network, we were
able to demonstrate that VoteRank-LRed improved performance in choosing
influence spreaders more efficiently than the original VoteRank on the tested
citation network.Comment: 10 pages, 3 figure
Identifying influential spreaders in complex networks based on gravity formula
How to identify the influential spreaders in social networks is crucial for
accelerating/hindering information diffusion, increasing product exposure,
controlling diseases and rumors, and so on. In this paper, by viewing the
k-shell value of each node as its mass and the shortest path distance between
two nodes as their distance, then inspired by the idea of the gravity formula,
we propose a gravity centrality index to identify the influential spreaders in
complex networks. The comparison between the gravity centrality index and some
well-known centralities, such as degree centrality, betweenness centrality,
closeness centrality, and k-shell centrality, and so forth, indicates that our
method can effectively identify the influential spreaders in real networks as
well as synthetic networks. We also use the classical
Susceptible-Infected-Recovered (SIR) epidemic model to verify the good
performance of our method.Comment: 4 tables and 4 figures, accepted by Physica
Finding important edges in networks through local information
In transportation, communication, social and other real complex networks,
some critical edges act a pivotal part in controlling the flow of information
and maintaining the integrity of the structure. Due to the importance of
critical edges in theoretical studies and practical applications, the
identification of critical edges gradually become a hot topic in current
researches. Considering the overlap of communities in the neighborhood of
edges, a novel and effective metric named subgraph overlap (SO) is proposed to
quantifying the significance of edges. The experimental results show that SO
outperforms all benchmarks in identifying critical edges which are crucial in
maintaining the integrity of the structure and functions of networks
Effects of human dynamics on epidemic spreading in C\^{o}te d'Ivoire
Understanding and predicting outbreaks of contagious diseases are crucial to
the development of society and public health, especially for underdeveloped
countries. However, challenging problems are encountered because of complex
epidemic spreading dynamics influenced by spatial structure and human dynamics
(including both human mobility and human interaction intensity). We propose a
systematical model to depict nationwide epidemic spreading in C\^{o}te
d'Ivoire, which integrates multiple factors, such as human mobility, human
interaction intensity, and demographic features. We provide insights to aid in
modeling and predicting the epidemic spreading process by data-driven
simulation and theoretical analysis, which is otherwise beyond the scope of
local evaluation and geometrical views. We show that the requirement that the
average local basic reproductive number to be greater than unity is not
necessary for outbreaks of epidemics. The observed spreading phenomenon can be
roughly explained as a heterogeneous diffusion-reaction process by redefining
mobility distance according to the human mobility volume between nodes, which
is beyond the geometrical viewpoint. However, the heterogeneity of human
dynamics still poses challenges to precise prediction
Identifying a set of influential spreaders in complex networks
Identifying a set of influential spreaders in complex networks plays a
crucial role in effective information spreading. A simple strategy is to choose
top- ranked nodes as spreaders according to influence ranking method such as
PageRank, ClusterRank and -shell decomposition. Besides, some heuristic
methods such as hill-climbing, SPIN, degree discount and independent set based
are also proposed. However, these approaches suffer from a possibility that
some spreaders are so close together that they overlap sphere of influence or
time consuming. In this report, we present a simply yet effectively iterative
method named VoteRank to identify a set of decentralized spreaders with the
best spreading ability. In this approach, all nodes vote in a spreader in each
turn, and the voting ability of neighbors of elected spreader will be decreased
in subsequent turn. Experimental results on four real networks show that under
Susceptible-Infected-Recovered (SIR) model, VoteRank outperforms the
traditional benchmark methods on both spreading speed and final affected scale.
What's more, VoteRank is also superior to other group-spreader identifying
methods on computational time.Comment: 13 pages, 6 Figures, 37 reference
Multicores-periphery structure in networks
Many real-world networks exhibit a multicores-periphery structure, with
densely connected vertices in multiple cores surrounded by a general periphery
of sparsely connected vertices. Identification of the multicores-periphery
structure can provide a new lens to understand the structures and functions of
various real-world networks. This paper defines the multicores-periphery
structure and introduces an algorithm to identify the optimal partition of
multiple cores and the periphery in general networks. We demonstrate the
performance of our algorithm by applying it to a well-known social network and
a patent technology network, which are best characterized by the
multicores-periphery structure. The analyses also reveal the differences
between our multicores-periphery detection algorithm and two state-of-the-art
algorithms for detecting the single core-periphery structure and community
structure.Comment: 26 page
Accurate ranking of influential spreaders in networks based on dynamically asymmetric link-impact
We propose an efficient and accurate measure for ranking spreaders and
identifying the influential ones in spreading processes in networks. While the
edges determine the connections among the nodes, their specific role in
spreading should be considered explicitly. An edge connecting nodes i and j may
differ in its importance for spreading from i to j and from j to i. The key
issue is whether node j, after infected by i through the edge, would reach out
to other nodes that i itself could not reach directly. It becomes necessary to
invoke two unequal weights wij and wji characterizing the importance of an edge
according to the neighborhoods of nodes i and j. The total asymmetric
directional weights originating from a node leads to a novel measure si which
quantifies the impact of the node in spreading processes. A s-shell
decomposition scheme further assigns a s-shell index or weighted coreness to
the nodes. The effectiveness and accuracy of rankings based on si and the
weighted coreness are demonstrated by applying them to nine real-world
networks. Results show that they generally outperform rankings based on the
nodes' degree and k-shell index, while maintaining a low computational
complexity. Our work represents a crucial step towards understanding and
controlling the spread of diseases, rumors, information, trends, and
innovations in networks.Comment: 9 pages, 8 figure
Leveraging local h-index to identify and rank influential spreaders in networks
Identifying influential nodes in complex networks has received increasing
attention for its great theoretical and practical applications in many fields.
Traditional methods, such as degree centrality, betweenness centrality,
closeness centrality, and coreness centrality, have more or less disadvantages
in detecting influential nodes, which have been illustrated in related
literatures. Recently, the h-index, which is utilized to measure both the
productivity and citation impact of the publications of a scientist or scholar,
has been introduced to the network world to evaluate a node's spreading
ability. However, this method assigns too many nodes with the same value, which
leads to a resolution limit problem in distinguishing the real influence of
these nodes. In this paper, we propose a local h-index centrality (LH-index)
method for identifying and ranking influential nodes in networks. The LH-index
method simultaneously takes into account of h-index values of the node itself
and its neighbors, which is based on the idea that a node connects to more
influential nodes will also be influential. According to the simulation results
with the stochastic Susceptible-Infected-Recovered (SIR) model in four real
world networks and several simulated networks, we demonstrate the effectivity
of the LH-index method in identifying influential nodes in networks.Comment: 15 pages,6 figure
Weighted H-index for identifying influential spreaders
Spreading is a ubiquitous process in the social, biological and technological
systems. Therefore, identifying influential spreaders, which is important to
prevent epidemic spreading and to establish effective vaccination strategies,
is full of theoretical and practical significance. In this paper, a weighted
h-index centrality based on virtual nodes extension is proposed to quantify the
spreading influence of nodes in complex networks. Simulation results on
real-world networks reveal that the proposed method provides more accurate and
more consistent ranking than the five classical methods. Moreover, we observe
that the monotonicity and the computational complexity of our measure can also
yield excellent performance