78 research outputs found
Complexity Framework for Forbidden Subgraphs IV: The Steiner Forest Problem
We study Steiner Forest on -subgraph-free graphs, that is, graphs that do
not contain some fixed graph as a (not necessarily induced) subgraph. We
are motivated by a recent framework that completely characterizes the
complexity of many problems on -subgraph-free graphs. However, in contrast
to e.g. the related Steiner Tree problem, Steiner Forest falls outside this
framework. Hence, the complexity of Steiner Forest on -subgraph-free graphs
remained tantalizingly open. In this paper, we make significant progress
towards determining the complexity of Steiner Forest on -subgraph-free
graphs. Our main results are four novel polynomial-time algorithms for
different excluded graphs that are central to further understand its
complexity. Along the way, we study the complexity of Steiner Forest for graphs
with a small -deletion set, that is, a small set of vertices such that
each component of has size at most . Using this parameter, we give two
noteworthy algorithms that we later employ as subroutines. First, we prove
Steiner Forest is FPT parameterized by when (i.e. the vertex cover
number). Second, we prove Steiner Forest is polynomial-time solvable for graphs
with a 2-deletion set of size at most 2. The latter result is tight, as the
problem is NP-complete for graphs with a 3-deletion set of size 2
Analysing Human Mobility Patterns of Hiking Activities through Complex Network Theory
The exploitation of high volume of geolocalized data from social sport
tracking applications of outdoor activities can be useful for natural resource
planning and to understand the human mobility patterns during leisure
activities. This geolocalized data represents the selection of hike activities
according to subjective and objective factors such as personal goals, personal
abilities, trail conditions or weather conditions. In our approach, human
mobility patterns are analysed from trajectories which are generated by hikers.
We propose the generation of the trail network identifying special points in
the overlap of trajectories. Trail crossings and trailheads define our network
and shape topological features. We analyse the trail network of Balearic
Islands, as a case of study, using complex weighted network theory. The
analysis is divided into the four seasons of the year to observe the impact of
weather conditions on the network topology. The number of visited places does
not decrease despite the large difference in the number of samples of the two
seasons with larger and lower activity. It is in summer season where it is
produced the most significant variation in the frequency and localization of
activities from inland regions to coastal areas. Finally, we compare our model
with other related studies where the network possesses a different purpose. One
finding of our approach is the detection of regions with relevant importance
where landscape interventions can be applied in function of the communities.Comment: 20 pages, 9 figures, accepte
A Graph Theoretic Perspective on Internet Topology Mapping
Understanding the topological characteristics of the Internet is an important research issue as the Internet grows with no central authority. Internet topology mapping studies help better understand the structure and dynamics of the Internet backbone. Knowing the underlying topology, researchers can better develop new protocols and services or fine-tune existing ones. Subnet-level Internet topology measurement studies involve three stages: topology collection, topology construction, and topology analysis. Each of these stages contains challenging tasks, especially when large-scale backbone topologies of millions of nodes are studied. In this dissertation, I first discuss issues in subnet-level Internet topology mapping and review state-of-the-art approaches to handle them. I propose a novel graph data indexing approach to to efficiently process large scale topology data. I then conduct an experimental study to understand how the responsiveness of routers has changed over the last decade and how it differs based on the probing mechanism. I then propose an efficient unresponsive resolution approach by incorporating our structural graph indexing technique. Finally, I introduce Cheleby, an integrated Internet topology mapping system. Cheleby first dynamically probes observed subnetworks using a team of PlanetLab nodes around the world to obtain comprehensive backbone topologies. Then, it utilizes efficient algorithms to resolve subnets, IP aliases, and unresponsive routers in the collected data sets to construct comprehensive subnet-level topologies. Sample topologies are provided at http://cheleby.cse.unr.edu
Complexity Framework for Forbidden Subgraphs IV: The Steiner Forest Problem
We study Steiner Forest on H-subgraph-free graphs, that is, graphs that do not contain some fixed graph H as a (not necessarily induced) subgraph. We are motivated by a recent framework that completely characterizes the complexity of many problems on H-subgraph-free graphs. However, in contrast to e.g. the related Steiner Tree problem, Steiner Forest falls outside this framework. Hence, the complexity of Steiner Forest on H-subgraph-free graphs remained tantalizingly open. In this paper, we make significant progress towards determining the complexity of Steiner Forest on H-subgraph-free graphs. Our main results are four novel polynomial-time algorithms for different excluded graphs H that are central to further understand its complexity. Along the way, we study the complexity of Steiner Forest for graphs with a small c-deletion set, that is, a small set S of vertices such that each component of G−S has size at most c. Using this parameter, we give two noteworthy algorithms that we later employ as subroutines. First, we prove Steiner Forest is FPT parameterized by |S| when c=1 (i.e. the vertex cover number). Second, we prove Steiner Forest is polynomial-time solvable for graphs with a 2-deletion set of size at most 2. The latter result is tight, as the problem is NP-complete for graphs with a 3-deletion set of size 2
Complex networks analysis in socioeconomic models
This chapter aims at reviewing complex networks models and methods that were
either developed for or applied to socioeconomic issues, and pertinent to the
theme of New Economic Geography. After an introduction to the foundations of
the field of complex networks, the present summary adds insights on the
statistical mechanical approach, and on the most relevant computational aspects
for the treatment of these systems. As the most frequently used model for
interacting agent-based systems, a brief description of the statistical
mechanics of the classical Ising model on regular lattices, together with
recent extensions of the same model on small-world Watts-Strogatz and
scale-free Albert-Barabasi complex networks is included. Other sections of the
chapter are devoted to applications of complex networks to economics, finance,
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issues, including results for opinion and citation networks.
Finally, some avenues for future research are introduced before summarizing the
main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared
for Complexity and Geographical Economics - Topics and Tools, P.
Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published
Using Graph Bayesian Neural Networks for fraud pattern detection and classification from bank transactions data
The thesis aims to conduct an investigation into money laundering networks by synthesizing many earlier methods. The methodology begins by training a Bayesian graph neural
network on a data set from DNB to learn the relationship between the money laundering
status of a transacting party and its features and network properties. One can then get node embeddings
by extracting the activation values of each prediction at the last hidden layer in the model. This will hopefully yield informative node embeddings that one could then visualize along with their predicted class and
associated uncertainty by applying dimensionality
reduction through Principal Component Analysis. If there are nodes that have the same predicted class and magnitude of uncertainty
that cluster together, one could try retrieving their associated networks and investigate the patterns
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