Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

Mining complex trees for hidden fruit : a graph–based computational solution to detect latent criminal networks : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Technology at Massey University, Albany, New Zealand.

The detection of crime is a complex and difficult endeavour. Public and private organisations – focusing on law enforcement, intelligence, and compliance – commonly apply the rational isolated actor approach premised on observability and materiality. This is manifested largely as conducting entity-level risk management sourcing ‘leads’ from reactive covert human intelligence sources and/or proactive sources by applying simple rules-based models. Focusing on discrete observable and material actors simply ignores that criminal activity exists within a complex system deriving its fundamental structural fabric from the complex interactions between actors - with those most unobservable likely to be both criminally proficient and influential. The graph-based computational solution developed to detect latent criminal networks is a response to the inadequacy of the rational isolated actor approach that ignores the connectedness and complexity of criminality. The core computational solution, written in the R language, consists of novel entity resolution, link discovery, and knowledge discovery technology. Entity resolution enables the fusion of multiple datasets with high accuracy (mean F-measure of 0.986 versus competitors 0.872), generating a graph-based expressive view of the problem. Link discovery is comprised of link prediction and link inference, enabling the high-performance detection (accuracy of ~0.8 versus relevant published models ~0.45) of unobserved relationships such as identity fraud. Knowledge discovery uses the fused graph generated and applies the “GraphExtract” algorithm to create a set of subgraphs representing latent functional criminal groups, and a mesoscopic graph representing how this set of criminal groups are interconnected. Latent knowledge is generated from a range of metrics including the “Super-broker” metric and attitude prediction. The computational solution has been evaluated on a range of datasets that mimic an applied setting, demonstrating a scalable (tested on ~18 million node graphs) and performant (~33 hours runtime on a non-distributed platform) solution that successfully detects relevant latent functional criminal groups in around 90% of cases sampled and enables the contextual understanding of the broader criminal system through the mesoscopic graph and associated metadata. The augmented data assets generated provide a multi-perspective systems view of criminal activity that enable advanced informed decision making across the microscopic mesoscopic macroscopic spectrum

Online Social Networks: Measurements, Analysis and Solutions for Mining Challenges

In the last decade, online social networks showed enormous growth. With the rise of these networks and the consequent availability of wealth social network data, Social Network Analysis (SNA) led researchers to get the opportunity to access, analyse and mine the social behaviour of millions of people, explore the way they communicate and exchange information. Despite the growing interest in analysing social networks, there are some challenges and implications accompanying the analysis and mining of these networks. For example, dealing with large-scale and evolving networks is not yet an easy task and still requires a new mining solution. In addition, finding communities within these networks is a challenging task and could open opportunities to see how people behave in groups on a large scale. Also, the challenge of validating and optimizing communities without knowing in advance the structure of the network due to the lack of ground truth is yet another challenging barrier for validating the meaningfulness of the resulting communities. In this thesis, we started by providing an overview of the necessary background and key concepts required in the area of social networks analysis. Our main focus is to provide solutions to tackle the key challenges in this area. For doing so, first, we introduce a predictive technique to help in the prediction of the execution time of the analysis tasks for evolving networks through employing predictive modeling techniques to the problem of evolving and large-scale networks. Second, we study the performance of existing community detection approaches to derive high quality community structure using a real email network through analysing the exchange of emails and exploring community dynamics. The aim is to study the community behavioral patterns and evaluate their quality within an actual network. Finally, we propose an ensemble technique for deriving communities using a rich internal enterprise real network in IBM that reflects real collaborations and communications between employees. The technique aims to improve the community detection process through the fusion of different algorithms

Considering User Intention in Differential Graph Queries

Empty answers are a major problem by processing pattern matching queries in graph databases. Especially, there can be multiple reasons why a query failed. To support users in such situations, differential queries can be used that deliver missing parts of a graph query. Multiple heuristics are proposed for differential queries, which reduce the search space. Although they are successful in increasing the performance, they can discard query subgraphs relevant to a user. To address this issue, the authors extend the concept of differential queries and introduce top-k differential queries that calculate the ranking based on users’ preferences and significantly support the users’ understanding of query database management systems. A user assigns relevance weights to elements of a graph query that steer the search and are used for the ranking. In this paper the authors propose different strategies for selection of relevance weights and their propagation. As a result, the search is modelled along the most relevant paths. The authors evaluate their solution and both strategies on the DBpedia data graph

Menetelmiä mielenkiintoisten solmujen löytämiseen verkostoista

With the increasing amount of graph-structured data available, finding interesting objects, i.e., nodes in graphs, becomes more and more important. In this thesis we focus on finding interesting nodes and sets of nodes in graphs or networks. We propose several definitions of node interestingness as well as different methods to find such nodes. Specifically, we propose to consider nodes as interesting based on their relevance and non-redundancy or representativeness w.r.t. the graph topology, as well as based on their characterisation for a class, such as a given node attribute value. Identifying nodes that are relevant, but non-redundant to each other is motivated by the need to get an overview of different pieces of information related to a set of given nodes. Finding representative nodes is of interest, e.g. when the user needs or wants to select a few nodes that abstract the large set of nodes. Discovering nodes characteristic for a class helps to understand the causes behind that class. Next, four methods are proposed to find a representative set of interesting nodes. The first one incrementally picks one interesting node after another. The second iteratively changes the set of nodes to improve its overall interestingness. The third method clusters nodes and picks a medoid node as a representative for each cluster. Finally, the fourth method contrasts diverse sets of nodes in order to select nodes characteristic for their class, even if the classes are not identical across the selected nodes. The first three methods are relatively simple and are based on the graph topology and a similarity or distance function for nodes. For the second and third, the user needs to specify one parameter, either an initial set of k nodes or k, the size of the set. The fourth method assumes attributes and class attributes for each node, a class-related interesting measure, and possible sets of nodes which the user wants to contrast, such as sets of nodes that represent different time points. All four methods are flexible and generic. They can, in principle, be applied on any weighted graph or network regardless of what nodes, edges, weights, or attributes represent. Application areas for the methods developed in this thesis include word co-occurrence networks, biological networks, social networks, data traffic networks, and the World Wide Web. As an illustrating example, consider a word co-occurrence network. There, finding terms (nodes in the graph) that are relevant to some given nodes, e.g. branch and root, may help to identify different, shared contexts such as botanics, mathematics, and linguistics. A real life application lies in biology where finding nodes (biological entities, e.g. biological processes or pathways) that are relevant to other, given nodes (e.g. some genes or proteins) may help in identifying biological mechanisms that are possibly shared by both the genes and proteins.Väitöskirja käsittelee verkostojen louhinnan menetelmiä. Sen tavoitteena on löytää mielenkiintoisia tietoja painotetuista verkoista. Painotettuna verkkona voi tarkastella esim. tekstiainestoja, biologisia ainestoja, ihmisten välisiä yhteyksiä tai internettiä. Tällaisissa verkoissa solmut edustavat käsitteitä (esim. sanoja, geenejä, ihmisiä tai internetsivuja) ja kaaret niiden välisiä suhteita (esim. kaksi sanaa esiintyy samassa lauseessa, geeni koodaa proteiinia, ihmisten ystävyyksiä tai internetsivu viittaa toiseen internetsivuun). Kaarten painot voivat vastata esimerkiksi yhteyden voimakuutta tai luotettavuutta. Väitöskirjassa esitetään erilaisia verkon rakenteeseen tai solmujen attribuutteihin perustuvia määritelmiä solmujen mielenkiintoisuudelle sekä useita menetelmiä mielenkiintoisten solmujen löytämiseksi. Mielenkiintoisuuden voi määritellä esim. merkityksellisyytenä suhteessa joihinkin annettuihin solmuihin ja toisaalta mielenkiintoisten solmujen keskinäisenä erilaisuutena. Esimerkiksi ns. ahneella menetelmällä voidaan löytää keskenään erilaisia solmuja yksi kerrallaan. Väitöskirjan tuloksia voidaan soveltaa esimerkiksi tekstiaineistoa käsittelemällä saatuun sanojen väliseen verkostoon, jossa kahden sanan välillä on sitä voimakkaampi yhteys mitä useammin ne tapaavat esiintyä keskenään samoissa lauseissa. Sanojen erilaisia käyttöyhteyksiä ja jopa merkityksiä voidaan nyt löytää automaattisesti. Jos kohdesanaksi otetaan vaikkapa "juuri", niin siihen liittyviä mutta keskenään toisiinsa liittymättömiä sanoja ovat "puu" (biologinen merkitys: kasvin juuri), "yhtälö" (matemaattinen merkitys: yhtälön ratkaisu eli juuri) sekä "indoeurooppalainen" (kielitieteellinen merkitys: sanan vartalo eli juuri). Tällaisia menetelmiä voidaan soveltaa esimerkiksi hakukoneessa: sanalla "juuri" tehtyihin hakutuloksiin sisällytetään tuloksia mahdollisimman erilaisista käyttöyhteyksistä, jotta käyttäjän tarkoittama merkitys tulisi todennäköisemmin katetuksi hakutuloksissa. Merkittävä sovelluskohde väitöskirjan menetelmille ovat biologiset verkot, joissa solmut edustavat biologisia käsitteitä (esim. geenejä, proteiineja tai sairauksia) ja kaaret niiden välisiä suhteita (esim. geeni koodaa proteiinia tai proteiini on aktiivinen tietyssä sairauksessa). Menetelmillä voidaan etsiä esimerkiksi sairauksiin vaikuttavia biologisia mekanismeja paikantamalla edustava joukko sairauteen ja siihen mahdollisesti liittyviin geeneihin verkostossa kytkeytyviä muita solmuja. Nämä voivat auttaa biologeja ymmärtämään geenien ja sairauden mahdollisia kytköksiä ja siten kohdentamaan jatkotutkimustaan lupaavimpiin geeneihin, proteiineihin tms. Väitöskirjassa esitetyt solmujen mielenkiintoisuuden määritelmät sekä niiden löytämiseen ehdotetut menetelmät ovat yleispäteviä ja niitä voi soveltaa periaatteessa mihin tahansa verkkoon riippumatta siitä, mitä solmut, kaaret tai painot edustavat. Kokeet erilaisilla verkoilla osoittavat että ne löytävät mielenkiintoisia solmuja

Recent advances in clustering methods for protein interaction networks

The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that clustering protein interaction network is an effective approach for identifying protein complexes or functional modules, which has become a major research topic in systems biology. In this review, recent advances in clustering methods for protein interaction networks will be presented in detail. The predictions of protein functions and interactions based on modules will be covered. Finally, the performance of different clustering methods will be compared and the directions for future research will be discussed