Finding role communities in directed networks using Role-Based Similarity, Markov Stability and the Relaxed Minimum Spanning Tree

We present a framework to cluster nodes in directed networks according to their roles by combining Role-Based Similarity (RBS) and Markov Stability, two techniques based on flows. First we compute the RBS matrix, which contains the pairwise similarities between nodes according to the scaled number of in- and out-directed paths of different lengths. The weighted RBS similarity matrix is then transformed into an undirected similarity network using the Relaxed Minimum-Spanning Tree (RMST) algorithm, which uses the geometric structure of the RBS matrix to unblur the network, such that edges between nodes with high, direct RBS are preserved. Finally, we partition the RMST similarity network into role-communities of nodes at all scales using Markov Stability to find a robust set of roles in the network. We showcase our framework through a biological and a man-made network.Comment: 4 pages, 2 figure

Interest communities and flow roles in directed networks: the Twitter network of the UK riots

Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection in networks through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e., groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer, and topic. The study of flows also allows us to generate an interest distance, which affords a personalised view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterised by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks.Comment: 32 pages, 14 figures. Supplementary Spreadsheet available from: http://www2.imperial.ac.uk/~mbegueri/Docs/riotsCommunities.zip or http://rsif.royalsocietypublishing.org/content/11/101/20140940/suppl/DC

An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions

Similar to community detection, partitioning the nodes of a network according to their structural roles aims to identify fundamental building blocks of a network. The found partitions can be used, e.g., to simplify descriptions of the network connectivity, to derive reduced order models for dynamical processes unfolding on processes, or as ingredients for various graph mining tasks. In this work, we offer a fresh look on the problem of role extraction and its differences to community detection and present a definition of node roles related to graph-isomorphism tests, the Weisfeiler-Leman algorithm and equitable partitions. We study two associated optimization problems (cost functions) grounded in ideas from graph isomorphism testing, and present theoretical guarantees associated to the solutions of these problems. Finally, we validate our approach via a novel "role-infused partition benchmark", a network model from which we can sample networks in which nodes are endowed with different roles in a stochastic way

The struggle for existence in the world market ecosystem

The global trade system can be viewed as a dynamic ecosystem in which exporters struggle for resources: the markets in which they export. We can think that the aim of an exporter is to gain the entirety of a market share (say, car imports from the United States). This is similar to the objective of an organism in its attempt to monopolize a given subset of resources in an ecosystem. In this paper, we adopt a multilayer network approach to describe this struggle. We use longitudinal, multiplex data on trade relations, spanning several decades. We connect two countries with a directed link if the source country's appearance in a market correlates with the target country's disappearing, where a market is defined as a country-product combination in a given decade. Each market is a layer in the network. We show that, by analyzing the countries' network roles in each layer, we are able to classify them as out-competing, transitioning or displaced. This classification is a meaningful one: when testing the future export patterns of these countries, we show that out-competing countries have distinctly stronger growth rates than the other two classes

Deep Learning and Deep Reinforcement Learning for Graph Based Applications

Dyp læring har gitt state-of-the-art ytelse i mange applikasjoner som datasyn, tekstanalyse, biologi, osv. Suksessen med dyp læring har også hjulpet fremveksten av dyp forsterkende læring for optimal beslutningstaking og har vist stort potensiale, spesielt i optimaliseringsproblemer. I tillegg har grafer som matematisk representasjon for strukturerte komplekse systemer vist seg å være et kraftig verktøy for analyse og problemløsning, og gitt et nytt perspektiv på formuleringen av problemet. Ved å introdusere grafer som en inputmodalitet for maskinlæringsproblemer kan dyplæringsmodeller enten bruke strukturen til grafen i sine representasjonslæringsskjema, eller optimalisere grafstrukturen i en nedstrøms evalueringsoppgave. Dette vil også føre til modellmetoder og pipelines som utnytter den strukturelle informasjonen gitt av grafer til forbedret ytelse, sammenlignet med tradisjonelle maskinlæringsmodellers kapasitet. I denne oppgaven introduserer vi fem forskjellige use-case-applikasjoner, gjennom fem forskningsartikler, som kan modelleres som grafer og tar sikte på å skape nye modeller som adresserer problemer ved bruk av dyp grafrepresentasjonslæring og dype forsterkningslæringsmodeller. Våre tre viktigste applikasjonsdomener er bioinformatikk, datasyn og logistikk. Først tar vi sikte på å adressere to problemer innen bioinformatikk. I Paper I tar vi opp spørsmålet om integrering av kontinuerlige omics-datasett med biologiske nettverk. Vi introduserer et auto-koderskjema fokusert på representasjonslæring av nodefunksjoner i biologiske nettverk, og viser anvendelsen av det utformede rammeverket i et virkelighetseksempel gjennom imputering av manglende verdier i et eksempeldatasett for omics. Paper II ser på bruk av grafrepresentasjonslæring for å behandle metabolske nettverk. I den foreslåtte tilnærmingen introduserer vi en maskinlæringspipeline (fra funksjonsekstraksjon til modellarkitektur) basert på grafiske nevrale nettverk og evaluerer pipelinen basert på prediksjon av genessensalitet, som er en velkjent bruk av metabolske banenettverk. Det andre domenet av applikasjoner er datasynsdomenet, spesifikt problemet med gjenkjennelse av menneskelige gester. I Paper III, og oppfølgingen Paper IV, introduserer vi et gestgjenkjenningssystem som er både raskere og mer nøyaktig enn den avanserte prediksjonen av menneskelige motivbevegelser fra mmWave Radar genererte punktskyer. Vi oppnår dette ved å modellere inngangspunktskyen som en spatio-temporal graf og å bearbeide den opprettede grafen ved bruk av den foreslåtte læringsteknikken for grafrepresentasjon. Videre evaluerer vi systemet under forskjellige eksperimentelle forhold ut ifra vinkelen til emnet med hensyn til sansing, og foreslår en ensembletilnærming for å dempe effekten av å endre sansevinkelen på ytelsen til modellen. Den siste applikasjonen vi tar for oss er bruken av dyp forsterkningslæring for å optimalisere strukturen til grafene i kombinatoriske optimaliseringsproblemer i logistikk. Paper V introduserer en generell problemuavhengig hyperheuristikk som utnytter beslutningsevnen til dyp forsterkende læring, ved å bruke en problemuavhengig tilstandsfunksjonsinformasjon. Det foreslåtte rammeverket er trent på en generell belønningsfunksjon for å oppnå høykvalitets ytelse blant populære løsere innen kombinatorisk optimalisering. Vi evaluerer ytelsen til den foreslåtte tilnærmingen med tre eksempler på ruting problemer samt et planleggingsproblem, for å vise effektiviteten til metoden vår i forskjellige typer problemstillinger.Deep learning has provided state-of-the-art performance in many applications such as computer vision, text analysis, biology, etc. The success of deep learning has also helped with the emergence of deep reinforcement learning for optimal decision-making and has shown great promise, especially in optimization problems. Additionally, graphs as a mathematical representation for structured complex systems have proven to be a powerful tool for analysis and problem-solving that offer a fresh perspective on the formulation of the problem. Introducing graphs as an input modality for machine learning problems enables deep learning models to either utilize the structure of the graph in their representation learning scheme or optimize the graph structure for a downstream evaluation task. Doing so will also lead to model methods and pipelines that leverage the structural information provided by graphs to improve performance compared to traditional machine learning models. In this thesis, we introduce five different use-case applications, in the format of five research papers, that can be modeled as graphs and aim to provide novel models that address problems using deep graph representation learning and deep reinforcement learning models. Our main three application domains are bioinformatics, computer vision, and logistics. First, we aim to address two problems in the domain of bioinformatics. In Paper I, we address the issue of integration of continuous omics datasets with biological networks. We introduce an auto-encoder scheme focused on representation learning of node features in biological networks and showcase the application of the designed framework in a real-world example through the imputation of missing values in an example omics dataset. Paper II looks at utilizing graph representation learning for processing metabolic networks. In the proposed approach, we introduce a machine learning pipeline (from feature extraction to model architecture) based on graph neural networks and evaluate the pipeline on the task of gene essentiality prediction which is a well-known application of metabolic pathway networks. The second domain of applications is the computer vision domain specifically the problem of human gesture recognition. In Paper III and the follow-up Paper IV, we introduce a gesture recognition system that is both faster and more accurate compared to the state-of-the-art prediction of human subject gestures from mmWave Radar generated point clouds. We achieve this by modeling the input point cloud as a spatio-temporal graph and processing the created graph using the proposed graph representation learning technique. We further evaluate the system in different experimental conditions in terms of the angle of the subject with respect to sensing and propose an ensemble approach for mitigating the effect of changing the sensing angle on the performance of the model. The last application that we address is the use of deep reinforcement learning to optimize the structure of the graphs in combinatorial optimization problems in logistics. Paper V introduces a general problem-independent hyperheuristic that utilizes the decision-making capability of deep reinforcement learning using a problem-independent state feature information. The proposed framework is trained on a general reward function to achieve state-of-the-art performance among popular solvers in the field of combinatorial optimization. We evaluate the performance of the proposed approach on three example routing problems as well as a scheduling problem to showcase the effectiveness of our method in different problems.Doktorgradsavhandlin