46 research outputs found
Feature learning in feature-sample networks using multi-objective optimization
Data and knowledge representation are fundamental concepts in machine
learning. The quality of the representation impacts the performance of the
learning model directly. Feature learning transforms or enhances raw data to
structures that are effectively exploited by those models. In recent years,
several works have been using complex networks for data representation and
analysis. However, no feature learning method has been proposed for such
category of techniques. Here, we present an unsupervised feature learning
mechanism that works on datasets with binary features. First, the dataset is
mapped into a feature--sample network. Then, a multi-objective optimization
process selects a set of new vertices to produce an enhanced version of the
network. The new features depend on a nonlinear function of a combination of
preexisting features. Effectively, the process projects the input data into a
higher-dimensional space. To solve the optimization problem, we design two
metaheuristics based on the lexicographic genetic algorithm and the improved
strength Pareto evolutionary algorithm (SPEA2). We show that the enhanced
network contains more information and can be exploited to improve the
performance of machine learning methods. The advantages and disadvantages of
each optimization strategy are discussed.Comment: 7 pages, 4 figure
Network community detection via iterative edge removal in a flocking-like system
We present a network community-detection technique based on properties that
emerge from a nature-inspired system of aligning particles. Initially, each
vertex is assigned a random-direction unit vector. A nonlinear dynamic law is
established so that neighboring vertices try to become aligned with each other.
After some time, the system stops and edges that connect the least-aligned
pairs of vertices are removed. Then the evolution starts over without the
removed edges, and after enough number of removal rounds, each community
becomes a connected component. The proposed approach is evaluated using
widely-accepted benchmarks and real-world networks. Experimental results reveal
that the method is robust and excels on a wide variety of networks. Moreover,
for large sparse networks, the edge-removal process runs in quasilinear time,
which enables application in large-scale networks
DHLP 1&2: Giraph based distributed label propagation algorithms on heterogeneous drug-related networks
Background and Objective: Heterogeneous complex networks are large graphs
consisting of different types of nodes and edges. The knowledge extraction from
these networks is complicated. Moreover, the scale of these networks is
steadily increasing. Thus, scalable methods are required. Methods: In this
paper, two distributed label propagation algorithms for heterogeneous networks,
namely DHLP-1 and DHLP-2 have been introduced. Biological networks are one type
of the heterogeneous complex networks. As a case study, we have measured the
efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network
consisting of drugs, diseases, and targets. The subject we have studied in this
network is drug repositioning but our algorithms can be used as general methods
for heterogeneous networks other than the biological network. Results: We
compared the proposed algorithms with similar non-distributed versions of them
namely MINProp and Heter-LP. The experiments revealed the good performance of
the algorithms in terms of running time and accuracy.Comment: Source code available for Apache Giraph on Hadoo
A Network Theory Approach to the Sharing Economy
With the rapid growth of businesses in the sharing economy, evidence is accumulating regarding their underlying business models, growth patterns and other characteristics.This article demonstrates that a network theory approach can be useful for analysing the internal structure and other features of sharing economy platforms and the networks created by them. After introducing the most important concepts and theoretical considerations relating to the sharing economy, we analyse the data of a regional ride share company based in Hungary. Our analysis reveals an increasingly popular service, which is in a phase of rapid growth in terms of both the number of origin/destination settlements and the number of trips/passengers. Taking settlements as nodes and trips between them as edges we demonstrate that the network formed by them shows the characteristics of scale-free networks.Our findings may help company managers and policy makers to fine tune their decisions and indicate potential areas for further research directions to better understand the societal effects of the sharing economy
A Novel Reinforcement Learning Routing Algorithm for Congestion Control in Complex Networks
Despite technological advancements, the significance of interdisciplinary
subjects like complex networks has grown. Exploring communication within these
networks is crucial, with traffic becoming a key concern due to the expanding
population and increased need for connections. Congestion tends to originate in
specific network areas but quickly proliferates throughout. Consequently,
understanding the transition from a flow-free state to a congested state is
vital. Numerous studies have delved into comprehending the emergence and
control of congestion in complex networks, falling into three general
categories: soft strategies, hard strategies, and resource allocation
strategies. This article introduces a routing algorithm leveraging
reinforcement learning to address two primary objectives: congestion control
and optimizing path length based on the shortest path algorithm, ultimately
enhancing network throughput compared to previous methods. Notably, the
proposed method proves effective not only in Barab\'asi-Albert scale-free
networks but also in other network models such as Watts-Strogatz (small-world)
and Erd\"os-R\'enyi (random network). Simulation experiment results demonstrate
that, across various traffic scenarios and network topologies, the proposed
method can enhance efficiency criteria by up to 30% while reducing maximum node
congestion by five times.Comment: 15 pages, 8 figures, under review at Journal of Systems Science &
Complexit