1,699 research outputs found
Regular decomposition of large graphs and other structures: scalability and robustness towards missing data
A method for compression of large graphs and matrices to a block structure is
further developed. Szemer\'edi's regularity lemma is used as a generic
motivation of the significance of stochastic block models. Another ingredient
of the method is Rissanen's minimum description length principle (MDL). We
continue our previous work on the subject, considering cases of missing data
and scaling of algorithms to extremely large size of graphs. In this way it
would be possible to find out a large scale structure of a huge graphs of
certain type using only a tiny part of graph information and obtaining a
compact representation of such graphs useful in computations and visualization.Comment: Accepted for publication in: Fourth International Workshop on High
Performance Big Graph Data Management, Analysis, and Mining, December 11,
2017, Bosto U.S.
Network-based approaches to explore complex biological systems towards network medicine
Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes
The State-of-the-Art of Set Visualization
Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net
Deep learning systems as complex networks
Thanks to the availability of large scale digital datasets and massive
amounts of computational power, deep learning algorithms can learn
representations of data by exploiting multiple levels of abstraction. These
machine learning methods have greatly improved the state-of-the-art in many
challenging cognitive tasks, such as visual object recognition, speech
processing, natural language understanding and automatic translation. In
particular, one class of deep learning models, known as deep belief networks,
can discover intricate statistical structure in large data sets in a completely
unsupervised fashion, by learning a generative model of the data using
Hebbian-like learning mechanisms. Although these self-organizing systems can be
conveniently formalized within the framework of statistical mechanics, their
internal functioning remains opaque, because their emergent dynamics cannot be
solved analytically. In this article we propose to study deep belief networks
using techniques commonly employed in the study of complex networks, in order
to gain some insights into the structural and functional properties of the
computational graph resulting from the learning process.Comment: 20 pages, 9 figure
A new theory of space syntax
Relations between different components of urban structure are often measured in aliteral manner, along streets for example, the usual representation being routesbetween junctions which form the nodes of an equivalent planar graph. A popularvariant on this theme ? space syntax ? treats these routes as streets containing one ormore junctions, with the equivalent graph representation being more abstract, basedon relations between the streets which themselves are treated as nodes. In this paper,we articulate space syntax as a specific case of relations between any two sets, in thiscase, streets and their junctions, from which we derive two related representations.The first or primal problem is traditional space syntax based on relations betweenstreets through their junctions; the second or dual problem is the more usualmorphological representation of relations between junctions through their streets.The unifying framework that we propose suggests we shift our focus from the primalproblem where accessibility or distance is associated with lines or streets, to the dualproblem where accessibility is associated with points or junctions. This traditionalrepresentation of accessibility between points rather than between lines is easier tounderstand and makes more sense visually. Our unifying framework enables us toeasily shift from the primal problem to the dual and back, thus providing a muchricher interpretation of the syntax. We develop an appropriate algebra which providesa clearer approach to connectivity and distance in the equivalent graphrepresentations, and we then demonstrate these variants for the primal and dualproblems in one of the first space syntax street network examples, the French villageof Gassin. An immediate consequence of our analysis is that we show how the directconnectivity of streets (or junctions) to one another is highly correlated with thedistance measures used. This suggests that a simplified form of syntax can beoperationalized through counts of streets and junctions in the original street network
Explorative Graph Visualization
Netzwerkstrukturen (Graphen) sind heutzutage weit verbreitet. Ihre Untersuchung dient dazu, ein besseres Verständnis ihrer Struktur und der durch sie modellierten realen Aspekte zu gewinnen. Die Exploration solcher Netzwerke wird zumeist mit Visualisierungstechniken unterstützt. Ziel dieser Arbeit ist es, einen Überblick über die Probleme dieser Visualisierungen zu geben und konkrete Lösungsansätze aufzuzeigen. Dabei werden neue Visualisierungstechniken eingeführt, um den Nutzen der geführten Diskussion für die explorative Graphvisualisierung am konkreten Beispiel zu belegen.Network structures (graphs) have become a natural part of everyday life and their analysis helps to gain an understanding of their inherent structure and the real-world aspects thereby expressed. The exploration of graphs is largely supported and driven by visual means. The aim of this thesis is to give a comprehensive view on the problems associated with these visual means and to detail concrete solution approaches for them. Concrete visualization techniques are introduced to underline the value of this comprehensive discussion for supporting explorative graph visualization
Learning Vertex Representations for Bipartite Networks
Recent years have witnessed a widespread increase of interest in network
representation learning (NRL). By far most research efforts have focused on NRL
for homogeneous networks like social networks where vertices are of the same
type, or heterogeneous networks like knowledge graphs where vertices (and/or
edges) are of different types. There has been relatively little research
dedicated to NRL for bipartite networks. Arguably, generic network embedding
methods like node2vec and LINE can also be applied to learn vertex embeddings
for bipartite networks by ignoring the vertex type information. However, these
methods are suboptimal in doing so, since real-world bipartite networks concern
the relationship between two types of entities, which usually exhibit different
properties and patterns from other types of network data. For example,
E-Commerce recommender systems need to capture the collaborative filtering
patterns between customers and products, and search engines need to consider
the matching signals between queries and webpages
Multilayer Networks
In most natural and engineered systems, a set of entities interact with each
other in complicated patterns that can encompass multiple types of
relationships, change in time, and include other types of complications. Such
systems include multiple subsystems and layers of connectivity, and it is
important to take such "multilayer" features into account to try to improve our
understanding of complex systems. Consequently, it is necessary to generalize
"traditional" network theory by developing (and validating) a framework and
associated tools to study multilayer systems in a comprehensive fashion. The
origins of such efforts date back several decades and arose in multiple
disciplines, and now the study of multilayer networks has become one of the
most important directions in network science. In this paper, we discuss the
history of multilayer networks (and related concepts) and review the exploding
body of work on such networks. To unify the disparate terminology in the large
body of recent work, we discuss a general framework for multilayer networks,
construct a dictionary of terminology to relate the numerous existing concepts
to each other, and provide a thorough discussion that compares, contrasts, and
translates between related notions such as multilayer networks, multiplex
networks, interdependent networks, networks of networks, and many others. We
also survey and discuss existing data sets that can be represented as
multilayer networks. We review attempts to generalize single-layer-network
diagnostics to multilayer networks. We also discuss the rapidly expanding
research on multilayer-network models and notions like community structure,
connected components, tensor decompositions, and various types of dynamical
processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure
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