251 research outputs found

    I was there!:Pop venues and festivals and their value in the ecosystem of live music

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    Robustness, Heterogeneity and Structure Capturing for Graph Representation Learning and its Application

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    Graph neural networks (GNNs) are potent methods for graph representation learn- ing (GRL), which extract knowledge from complicated (graph) structured data in various real-world scenarios. However, GRL still faces many challenges. Firstly GNN-based node classification may deteriorate substantially by overlooking the pos- sibility of noisy data in graph structures, as models wrongly process the relation among nodes in the input graphs as the ground truth. Secondly, nodes and edges have different types in the real-world and it is essential to capture this heterogeneity in graph representation learning. Next, relations among nodes are not restricted to pairwise relations and it is necessary to capture the complex relations accordingly. Finally, the absence of structural encodings, such as positional information, deterio- rates the performance of GNNs. This thesis proposes novel methods to address the aforementioned problems: 1. Bayesian Graph Attention Network (BGAT): Developed for situations with scarce data, this method addresses the influence of spurious edges. Incor- porating Bayesian principles into the graph attention mechanism enhances robustness, leading to competitive performance against benchmarks (Chapter 3). 2. Neighbour Contrastive Heterogeneous Graph Attention Network (NC-HGAT): By enhancing a cutting-edge self-supervised heterogeneous graph neural net- work model (HGAT) with neighbour contrastive learning, this method ad- dresses heterogeneity and uncertainty simultaneously. Extra attention to edge relations in heterogeneous graphs also aids in subsequent classification tasks (Chapter 4). 3. A novel ensemble learning framework is introduced for predicting stock price movements. It adeptly captures both group-level and pairwise relations, lead- ing to notable advancements over the existing state-of-the-art. The integration of hypergraph and graph models, coupled with the utilisation of auxiliary data via GNNs before recurrent neural network (RNN), provides a deeper under- standing of long-term dependencies between similar entities in multivariate time series analysis (Chapter 5). 4. A novel framework for graph structure learning is introduced, segmenting graphs into distinct patches. By harnessing the capabilities of transformers and integrating other position encoding techniques, this approach robustly capture intricate structural information within a graph. This results in a more comprehensive understanding of its underlying patterns (Chapter 6)

    A Connected World. Social Networks and Organizations

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    This is the submitted version. The final version is available from Cambridge University Press via the DOI in this recordThis Element synthesizes the current state of research on organizational social networks from its early foundations to contemporary debates. It highlights the characteristics that make the social network perspective distinctive in the organizational research landscape, including its emphasis on structure and outcomes. It covers the main theoretical developments and summarizes the research design questions that organizational researchers face when collecting and analyzing network data. Then, it discusses current debates ranging from agency and structure to network volatility and personality. Finally, the Element envisages future research directions on the role of brokerage for individuals and communities, network cognition, and the importance of past ties. Overall, the Element provides an innovative angle for understanding organizational social networks, engaging in empirical network research, and nurturing further theoretical development on the role of social interactions and connectedness in modern organizations

    A Connected World: Social Networks and Organizations

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    This Element synthesizes the current state of research on organizational social networks from its early foundations to contemporary debates. It highlights the characteristics that make the social network perspective distinctive in the organizational research landscape, including its emphasis on structure and outcomes. It covers the main theoretical developments and summarizes the research design questions that organizational researchers face when collecting and analyzing network data. Then, it discusses current debates ranging from agency and structure to network volatility and personality. Finally, the Element envisages future research directions on the role of brokerage for individuals and communities, network cognition, and the importance of past ties. Overall, the Element provides an innovative angle for understanding organizational social networks, engaging in empirical network research, and nurturing further theoretical development on the role of social interactions and connectedness in modern organizations

    Inferring network structures using hierarchical exponential random graph models

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    Networks are broadly used to represent the interaction relationships between entities in a wide range of scientific fields. Ensembles of networks are employed to provide multiple network observations from the same set of entities. These observations may capture different features of the relationships: some ensembles exhibit group structures; some ensembles are collected over time; other ensembles have more individual differences. Statistical models for ensembles of networks should describe not only the dependency structure within each network, but also the variations of the structural patterns across networks. Exponential random graph models (ERGMs) provide a highly flexible way to study the complex dependency structures within networks. We aim to develop novel methodologies that utilise ERGMs to infer the underlying structures of ensembles of networks from the following three aspects: (1) identifying and characterising groups of networks that are similar with respect to the effects of local connectivity patterns and covariates of interest on shaping the global structure of networks; (2) modelling the evolution of networks over time by representing the associated parameters using a piecewise linear function; (3) analysing the individual characteristics of each network and the population structures of the whole ensemble in terms of the block structure, homophily, transitivity and other local structural properties. For identifying the group structure of ensembles and the block structure of networks, we employ a Bayesian nonparametric prior on an infinite sample space, instead of requiring a fixed number of groups in advance as in the existing models. In this way, the number of mixture models can grow along with the data size. This appealing property enables our models to fit the data better. Moreover, for the ensembles of networks with a time order, we utilise a fused lasso penalty to encourage similarities on the parameter estimation of the consecutive networks as they tend to share similar connectivity patterns. The inference of ERGMs under a Bayesian nonparametric framework is very challenging due to the fact that we have an infinite number of intractable ERGM likelihood functions in the model. Besides, the dependency among edges within the same block and the unknown number of blocks also significantly increase the difficulty of recovering the unknown block structure. What's more, the correlation between dynamic networks also requires us to work on all the possible edges of the ensemble simultaneously, posing a big challenge for the algorithm. To solve these issues, we develop five algorithms for the model estimation: (1) a novel Metropolis-Hastings algorithm to sample from the intractable posterior distribution of ERGMs with multiple networks using an intermediate importance sampling technique; (2) a new Metropolis-within-slice sampling algorithm to perform full Bayesian inference on infinite mixtures of ERGMs; (3) a pseudo likelihood based Metropolis-within-slice sampling algorithm to learn the group structure of ensembles fast and adaptively; (4) an alternating direction method of multipliers (ADMM) algorithm for the fast estimation of dynamic ensembles using a matrix decomposition technique; (5) a Metropolis-within-Gibbs sampling algorithm for the population analysis of structural patterns with an approximated stick-breaking prior

    Embedded Topics in the Stochastic Block Model

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    Communication networks such as emails or social networks are now ubiquitous and their analysis has become a strategic field. In many applications, the goal is to automatically extract relevant information by looking at the nodes and their connections. Unfortunately, most of the existing methods focus on analysing the presence or absence of edges and textual data is often discarded. However, all communication networks actually come with textual data on the edges. In order to take into account this specificity, we consider in this paper networks for which two nodes are linked if and only if they share textual data. We introduce a deep latent variable model allowing embedded topics to be handled called ETSBM to simultaneously perform clustering on the nodes while modelling the topics used between the different clusters. ETSBM extends both the stochastic block model (SBM) and the embedded topic model (ETM) which are core models for studying networks and corpora, respectively. The inference is done using a variational-Bayes expectation-maximisation algorithm combined with a stochastic gradient descent. The methodology is evaluated on synthetic data and on a real world dataset

    Learning representations for graph-structured socio-technical systems

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    The recent widespread use of social media platforms and web services has led to a vast amount of behavioral data that can be used to model socio-technical systems. A significant part of this data can be represented as graphs or networks, which have become the prevalent mathematical framework for studying the structure and the dynamics of complex interacting systems. However, analyzing and understanding these data presents new challenges due to their increasing complexity and diversity. For instance, the characterization of real-world networks includes the need of accounting for their temporal dimension, together with incorporating higher-order interactions beyond the traditional pairwise formalism. The ongoing growth of AI has led to the integration of traditional graph mining techniques with representation learning and low-dimensional embeddings of networks to address current challenges. These methods capture the underlying similarities and geometry of graph-shaped data, generating latent representations that enable the resolution of various tasks, such as link prediction, node classification, and graph clustering. As these techniques gain popularity, there is even a growing concern about their responsible use. In particular, there has been an increased emphasis on addressing the limitations of interpretability in graph representation learning. This thesis contributes to the advancement of knowledge in the field of graph representation learning and has potential applications in a wide range of complex systems domains. We initially focus on forecasting problems related to face-to-face contact networks with time-varying graph embeddings. Then, we study hyperedge prediction and reconstruction with simplicial complex embeddings. Finally, we analyze the problem of interpreting latent dimensions in node embeddings for graphs. The proposed models are extensively evaluated in multiple experimental settings and the results demonstrate their effectiveness and reliability, achieving state-of-the-art performances and providing valuable insights into the properties of the learned representations

    Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks

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    We present a new representation learning framework, Intensity Profile Projection, for continuous-time dynamic network data. Given triples (i,j,t)(i,j,t), each representing a time-stamped (tt) interaction between two entities (i,ji,j), our procedure returns a continuous-time trajectory for each node, representing its behaviour over time. The framework consists of three stages: estimating pairwise intensity functions, e.g. via kernel smoothing; learning a projection which minimises a notion of intensity reconstruction error; and constructing evolving node representations via the learned projection. The trajectories satisfy two properties, known as structural and temporal coherence, which we see as fundamental for reliable inference. Moreoever, we develop estimation theory providing tight control on the error of any estimated trajectory, indicating that the representations could even be used in quite noise-sensitive follow-on analyses. The theory also elucidates the role of smoothing as a bias-variance trade-off, and shows how we can reduce the level of smoothing as the signal-to-noise ratio increases on account of the algorithm `borrowing strength' across the network.Comment: 37 pages, 10 figure
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