Hierarchical Message-Passing Graph Neural Networks

Graph Neural Networks (GNNs) have become a promising approach to machine learning with graphs. Since existing GNN models are based on flat message-passing mechanisms, two limitations need to be tackled. One is costly in encoding global information on the graph topology. The other is failing to model meso- and macro-level semantics hidden in the graph, such as the knowledge of institutes and research areas in an academic collaboration network. To deal with these two issues, we propose a novel Hierarchical Message-Passing Graph Neural Networks framework. The main idea is to generate a hierarchical structure that re-organises all nodes in a graph into multi-level clusters, along with intra- and inter-level edge connections. The derived hierarchy not only creates shortcuts connecting far-away nodes so that global information can be efficiently accessed via message passing but also incorporates meso- and macro-level semantics into the learning of node embedding. We present the first model to implement this hierarchical message-passing mechanism, termed Hierarchical Community-aware Graph Neural Network (HC-GNN), based on hierarchical communities detected from the graph. Experiments conducted on eight datasets under transductive, inductive, and few-shot settings exhibit that HC-GNN can outperform state-of-the-art GNN models in network analysis tasks, including node classification, link prediction, and community detection

Big networks : a survey

A network is a typical expressive form of representing complex systems in terms of vertices and links, in which the pattern of interactions amongst components of the network is intricate. The network can be static that does not change over time or dynamic that evolves through time. The complication of network analysis is different under the new circumstance of network size explosive increasing. In this paper, we introduce a new network science concept called a big network. A big networks is generally in large-scale with a complicated and higher-order inner structure. This paper proposes a guideline framework that gives an insight into the major topics in the area of network science from the viewpoint of a big network. We first introduce the structural characteristics of big networks from three levels, which are micro-level, meso-level, and macro-level. We then discuss some state-of-the-art advanced topics of big network analysis. Big network models and related approaches, including ranking methods, partition approaches, as well as network embedding algorithms are systematically introduced. Some typical applications in big networks are then reviewed, such as community detection, link prediction, recommendation, etc. Moreover, we also pinpoint some critical open issues that need to be investigated further. © 2020 Elsevier Inc

Investigating healthcare IT innovations:A ‘conceptual blending’ approach

PURPOSE: The purpose of this paper is to better understand how and why adoption and implementation of healthcare IT innovations occur. The authors examine two IT applications, computerised physician order entry (CPOE) and picture archiving and communication systems (PACS) at the meso and micro levels, within the context of the National Programme for IT in the English National Health Service (NHS). DESIGN/METHODOLOGY/APPROACH: To analyse these multi-level dynamics, the authors blend Rogers' diffusion of innovations theory (DoIT) with Webster's sociological critique of technological innovation in medicine and healthcare systems to illuminate a wider range of interacting factors. Qualitative data collected between 2004 and 2006 uses semi-structured, in-depth interviews with 72 stakeholders across four English NHS hospital trusts. FINDINGS: Overall, PACS was more successfully implemented (fully or partially in three out of four trusts) than CPOE (implemented in one trust only). Factors such as perceived benefit to users and attributes of the application - in particular speed, ease of use, reliability and flexibility and levels of readiness - were highly relevant but their influence was modulated through interaction with complex structural and relational issues. PRACTICAL IMPLICATIONS: Results reveal that combining contextual system level theories with DoIT increases understanding of real-life processes underpinning implementation of IT innovations within healthcare. They also highlight important drivers affecting success of implementation, including socio-political factors, the social body of practice and degree of "co-construction" between designers and end-users. ORIGINALITY/VALUE: The originality of the study partly rests on its methodological innovativeness and its value on critical insights afforded into understanding complex IT implementation programmes

Microsimulation of urban land use

The project ILUMASS (Integrated Land-Use Modelling and Transportation System Simulation) aims at embedding a microscopic dynamic simulation model of urban traffic flows into a comprehensive model system incorporating both changes of land use and the resulting changes in transport demand. The land-use component of ILUMASS will be based on the land-use parts of an existing urban simulation model, but is to be microscopic like the transport parts of ILUMASS. Microsimulation modules will include models of demographic development, household formation, firm lifecycles, residential and non-residential construction, labour mobility on the regional labour market and household mobility on the regional housing market. These modules will be closely linked with the models of daily activity patterns and travel and goods movements modelled in the transport parts of ILUMASS developed by other partners of the project team. The design of the land use model takes into account that the collection of individual micro data (i.e. data which because of their micro location can be associated with individual buildings or small groups of buildings) or the retrieval of individual micro data from administrative registers for planning purposes is neither possible nor, for privacy reasons, desirable. The land use model therefore works with synthetic micro data which can be retrieved from generally accessible public data. ILUMASS is a group project of institutes of the universities of Aachen, Bamberg, Dortmund, Cologne and Wuppertal under the co-ordination of the Transport Research Institute of the German Aerospace Centre (DLR). Study region for tests and first applications of the model is the urban region of Dortmund. The common database will be compiled in co-operation with the City of Dortmund. After its completion the integrated model is to be used for assessing the impacts of potential transport and land use policies for the new land use plan of the city. The paper will focus on the land-use parts of the ILUMASS model. It will present the underlying behavioural theories and how they are made operational in the model design, explain how the synthetic population is generated, show first model results and demonstrate the potential usefulness of the model for the planning process.

Multi-scale attributed node embedding

We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE). Capturing attribute-neighborhood relationships over multiple scales is useful for a diverse range of applications, including latent feature identification across disconnected networks with similar attributes. We prove theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings. Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social networks and web graphs.Comment: Published in the Journal of Complex Network

Interdiscursive Readings in Cultural Consumer Research

The cultural consumption research landscape of the 21st century is marked by an increasing cross-disciplinary fermentation. At the same time, cultural theory and analysis have been marked by successive ‘inter-’ turns, most notably with regard to the Big Four: multimodality (or intermodality), interdiscursivity, transmediality (or intermediality), and intertextuality. This book offers an outline of interdiscursivity as an integrative platform for accommodating these notions. To this end, a call for a return to Foucault is issued via a critical engagement with the so-called practice-turn. This re-turn does not seek to reconstitute venerably Foucauldianism, but to theorize ‘inters-’ as vanishing points that challenge the integrity of discrete cultural orders in non-convergent manners. The propounded interdiscursivity approach is offered as a reading strategy that permeates the contemporary cultural consumption phenomena that are scrutinized in this book, against a pan-consumptivist framework. By drawing on qualitative and mixed methods research designs, facilitated by CAQDAS software, the empirical studies that are hosted here span a vivid array of topics that are directly relevant to both traditional and new media researchers, such as the consumption of ideologies in Web 2.0 social movements, the ability of micro-celebrities to act as cultural game-changers, the post-loyalty abjective consumption ethos. The theoretically novel approaches on offer are coupled with methodological innovations in areas such as user-generated content, artists’ branding, and experiential consumption

Enhancing network embedding with implicit clustering

Network embedding aims at learning the low dimensional representation of nodes. These representations can be widely used for network mining tasks, such as link prediction, anomaly detection, and classification. Recently, a great deal of meaningful research work has been carried out on this emerging network analysis paradigm. The real- world network contains different size clusters because of the edges with different relationship types. These clusters also reflect some features of nodes, which can contribute to the optimization of the feature representation of nodes. However, existing network embedding methods do not distinguish these relationship types. In this paper, we propose an unsupervised network representation learning model that can encode edge relationship information. Firstly, an objective function is defined, which can learn the edge vectors by implicit clustering. Then, a biased random walk is designed to generate a series of node sequences, which are put into Skip-Gram to learn the low dimensional node representations. Extensive experiments are conducted on several network datasets. Compared with the state-of-art baselines, the proposed method is able to achieve favorable and stable results in multi-label classification and link prediction tasks