509 research outputs found
Ranking and clustering of nodes in networks with smart teleportation
Random teleportation is a necessary evil for ranking and clustering directed
networks based on random walks. Teleportation enables ergodic solutions, but
the solutions must necessarily depend on the exact implementation and
parametrization of the teleportation. For example, in the commonly used
PageRank algorithm, the teleportation rate must trade off a heavily biased
solution with a uniform solution. Here we show that teleportation to links
rather than nodes enables a much smoother trade-off and effectively more robust
results. We also show that, by not recording the teleportation steps of the
random walker, we can further reduce the effect of teleportation with dramatic
effects on clustering.Comment: 10 pages, 7 figure
Degree Landscapes in Scale-Free Networks
We generalize the degree-organizational view of real-world networks with
broad degree-distributions in a landscape analogue with mountains (high-degree
nodes) and valleys (low-degree nodes). For example, correlated degrees between
adjacent nodes corresponds to smooth landscapes (social networks), hierarchical
networks to one-mountain landscapes (the Internet), and degree-disassortative
networks without hierarchical features to rough landscapes with several
mountains. We also generate ridge landscapes to model networks organized under
constraints imposed by the space the networks are embedded in, associated to
spatial or, in molecular networks, to functional localization. To quantify the
topology, we here measure the widths of the mountains and the separation
between different mountains.Comment: 4 pages, 5 figure
A scale-free network hidden in the collapsing polymer
We show that the collapsed globular phase of a polymer accommodates a
scale-free incompatibility graph of its contacts. The degree distribution of
this network is found to decay with the exponent up to a
cut-off degree , where is the loop exponent for dense
polymers ( in two dimensions) and is the length of the polymer. Our
results exemplify how a scale-free network (SFN) can emerge from standard
criticality.Comment: 4 pages, 3 figures, address correcte
Information Horizons in Networks
We investigate and quantify the interplay between topology and ability to
send specific signals in complex networks. We find that in a majority of
investigated real-world networks the ability to communicate is favored by the
network topology on small distances, but disfavored at larger distances. We
further discuss how the ability to locate specific nodes can be improved if
information associated to the overall traffic in the network is available.Comment: Submitted top PR
Time walkers and spatial dynamics of ageing information
The distribution of information is essential for living system's ability to
coordinate and adapt. Random walkers are often used to model this distribution
process and, in doing so, one effectively assumes that information maintains
its relevance over time. But the value of information in social and biological
systems often decay and must continuously be updated. To capture the spatial
dynamics of ageing information, we introduce time walkers. A time walker moves
like a random walker, but interacts with traces left by other walkers, some
representing older information, some newer. The traces forms a navigable
information landscape. We quantify the dynamical properties of time walkers
moving on a two-dimensional lattice and the quality of the information
landscape generated by their movements. We visualise the self-similar landscape
as a river network, and show that searching in this landscape is superior to
random searching and scales as the length of loop-erased random walks
A customisable pipeline for continuously harvesting socially-minded Twitter users
On social media platforms and Twitter in particular, specific classes of
users such as influencers have been given satisfactory operational definitions
in terms of network and content metrics.
Others, for instance online activists, are not less important but their
characterisation still requires experimenting.
We make the hypothesis that such interesting users can be found within
temporally and spatially localised contexts, i.e., small but topical fragments
of the network containing interactions about social events or campaigns with a
significant footprint on Twitter.
To explore this hypothesis, we have designed a continuous user profile
discovery pipeline that produces an ever-growing dataset of user profiles by
harvesting and analysing contexts from the Twitter stream.
The profiles dataset includes key network and content-based users metrics,
enabling experimentation with user-defined score functions that characterise
specific classes of online users.
The paper describes the design and implementation of the pipeline and its
empirical evaluation on a case study consisting of healthcare-related campaigns
in the UK, showing how it supports the operational definitions of online
activism, by comparing three experimental ranking functions. The code is
publicly available.Comment: Procs. ICWE 2019, June 2019, Kore
Distributed Graph Clustering using Modularity and Map Equation
We study large-scale, distributed graph clustering. Given an undirected
graph, our objective is to partition the nodes into disjoint sets called
clusters. A cluster should contain many internal edges while being sparsely
connected to other clusters. In the context of a social network, a cluster
could be a group of friends. Modularity and map equation are established
formalizations of this internally-dense-externally-sparse principle. We present
two versions of a simple distributed algorithm to optimize both measures. They
are based on Thrill, a distributed big data processing framework that
implements an extended MapReduce model. The algorithms for the two measures,
DSLM-Mod and DSLM-Map, differ only slightly. Adapting them for similar quality
measures is straight-forward. We conduct an extensive experimental study on
real-world graphs and on synthetic benchmark graphs with up to 68 billion
edges. Our algorithms are fast while detecting clusterings similar to those
detected by other sequential, parallel and distributed clustering algorithms.
Compared to the distributed GossipMap algorithm, DSLM-Map needs less memory, is
up to an order of magnitude faster and achieves better quality.Comment: 14 pages, 3 figures; v3: Camera ready for Euro-Par 2018, more
details, more results; v2: extended experiments to include comparison with
competing algorithms, shortened for submission to Euro-Par 201
Outlier Edge Detection Using Random Graph Generation Models and Applications
Outliers are samples that are generated by different mechanisms from other
normal data samples. Graphs, in particular social network graphs, may contain
nodes and edges that are made by scammers, malicious programs or mistakenly by
normal users. Detecting outlier nodes and edges is important for data mining
and graph analytics. However, previous research in the field has merely focused
on detecting outlier nodes. In this article, we study the properties of edges
and propose outlier edge detection algorithms using two random graph generation
models. We found that the edge-ego-network, which can be defined as the induced
graph that contains two end nodes of an edge, their neighboring nodes and the
edges that link these nodes, contains critical information to detect outlier
edges. We evaluated the proposed algorithms by injecting outlier edges into
some real-world graph data. Experiment results show that the proposed
algorithms can effectively detect outlier edges. In particular, the algorithm
based on the Preferential Attachment Random Graph Generation model consistently
gives good performance regardless of the test graph data. Further more, the
proposed algorithms are not limited in the area of outlier edge detection. We
demonstrate three different applications that benefit from the proposed
algorithms: 1) a preprocessing tool that improves the performance of graph
clustering algorithms; 2) an outlier node detection algorithm; and 3) a novel
noisy data clustering algorithm. These applications show the great potential of
the proposed outlier edge detection techniques.Comment: 14 pages, 5 figures, journal pape
Community Structure Characterization
This entry discusses the problem of describing some communities identified in
a complex network of interest, in a way allowing to interpret them. We suppose
the community structure has already been detected through one of the many
methods proposed in the literature. The question is then to know how to extract
valuable information from this first result, in order to allow human
interpretation. This requires subsequent processing, which we describe in the
rest of this entry
Markov Chain Methods For Analyzing Complex Transport Networks
We have developed a steady state theory of complex transport networks used to
model the flow of commodity, information, viruses, opinions, or traffic. Our
approach is based on the use of the Markov chains defined on the graph
representations of transport networks allowing for the effective network
design, network performance evaluation, embedding, partitioning, and network
fault tolerance analysis. Random walks embed graphs into Euclidean space in
which distances and angles acquire a clear statistical interpretation. Being
defined on the dual graph representations of transport networks random walks
describe the equilibrium configurations of not random commodity flows on
primary graphs. This theory unifies many network concepts into one framework
and can also be elegantly extended to describe networks represented by directed
graphs and multiple interacting networks.Comment: 26 pages, 4 figure
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