Networks of communities and communities of networks in online government

Over the past decade the World Wide Web has become a core platform for the electronic operation of government. Yet the shape and nature of government presence on the Web and the online community in which it resides remains poorly understood and relatively under-theorised. This paper analyses large-scale web crawling data that map the hyperlink network structure between government websites and the broader Web ecology in the UK. In particular, it reports the ‘communities’ of websites within a hyperlink network of over 19,000 websites and over 135,000 hyperlinks derived from 75 key UK government seed sites at national, regional (i.e. Scotland and Wales) and local government levels. These website communities were derived by utilising Infomap, a state-of-the-art community detection algorithm that operate on the principle that flows of information in complex networks reveals community structure. Identifying and analysing online communities in which government websites reside provides insights in how hyperlink communities are arranged, that is, their emergent organizing principal and the importance of government in these online communities. It is hypothesized that online ‘communities’ can occur around different policy topics (such as health, education or policing), or along institutional or jurisdictional boundaries (such as England, Scotland and Wales). Using this novel approach this paper demonstrates that communities emerge on both axes, and that social media and government portals are some of the most significant communities based on information flows. This research provides foundational knowledge about the role of government websites in the World Wide Web, the emergent online associations, and the changing dynamic of state information in the twenty-first century. It points to strategies for developing government Web presence in networks that matter

Networks of Communities and Communities of Networks in Online Government

Over the past decade the World Wide Web has become a core platform for the electronic operation of government. Yet the shape and nature of government presence on the Web and the online community in which it resides remains poorly understood and under-theorised. This paper analyses large-scale web crawling data that map the hyperlink network structure between government websites and the broader Web ecology in the UK. In particular, it reports the ‘communities’ of websites within a hyperlink network of over 19,000 websites and over 135,000 hyperlinks derived from 75 key UK government seed sites at national, regional (i.e. Scotland and Wales) and local government levels. Website communities were derived by utilising Infomap, a state-of-the-art community detection algorithm that operates on the principle that flows of information in complex networks reveals community structure. Identifying and analysing online communities in which government websites reside provides insights in how hyperlink communities are arranged, that is, their emergent organizing principal and the importance of government in these online communities. It is hypothesized that online ‘communities’ can occur around different policy topics (such as health, education or policing), or along institutional or jurisdictional boundaries (such as England, Scotland and Wales). Using this novel approach this paper demonstrates that communities emerge on both axes, and that social media and government portals are some of the most significant communities based on information flows. This research provides foundational knowledge about the role of government websites in the World Wide Web, the emergent online associations, and the changing dynamic of state information in the twenty-first century. It points to strategies for developing government Web presence in networks that matter

Parallel Algorithms for Scalable Graph Mining: Applications on Big Data and Machine Learning

Parallel computing plays a crucial role in processing large-scale graph data. Complex network analysis is an exciting area of research for many applications in different scientific domains e.g., sociology, biology, online media, recommendation systems and many more. Graph mining is an area of interest with diverse problems from different domains of our daily life. Due to the advancement of data and computing technologies, graph data is growing at an enormous rate, for example, the number of links in social networks is growing every millisecond. Machine/Deep learning plays a significant role for technological accomplishments to work with big data in modern era. We work on a well-known graph problem, community detection (CD). We design parallelalgorithms for Louvain method for static networks and show around 12-fold speedup. The implementations use both shared-memory and distributed memory parallel algorithms. We also show the change of communities in dynamic networks in different time phases computing several graph metrics based on their temporal definition. We detect temporal communities in dynamicnetworks representing social/brain/communication/citation networks in a more concrete way. We present both shared-memory and distributed-memory parallel algorithms for CD in dynamic graphs using permanence, a vertex-based metric. The parallel CD algorithm implemented using Message Passing Interface (MPI) for temporal graphs is the first MPI-based algorithm to the best of our knowledge. Our algorithm achieves 30× speedup for the largest network with billions of edges. We present a scalable method for CD based on Graph Convolutional Network (GCN) via semi-supervised node classification using PyTorch with CUDA on GPU environment (4× performance gain). Our model achieves up to 86.9% accuracy and 0.85 F1 Score on different real-world datasets from diverse domains. We provide a scalable solution to the Sparse Deep Neural Network (DNN) Challenge by designing data parallel Sparse DNN using TensorFlow on GPU (4.7× speedup). We include the applications of webspam detection from webgraphs (billions of edges), sentiment analysis on social network, Twitter (1.2 million tweets) to reveal insights about COVID-19 vaccination awareness among the public and timeseries forecasting of the vaccinated population in the USA to portray the importance of graph mining in our daily activities

IDENTIFICATION OF PRACTICAL TRAFFIC VIA DIGITAL MEDIA TWITTER STREAM AND SCRUTINY

In the recent times, social networks have been extensively used as a data source for the event detection. Social networks permit people to generate an identity and allow them share it to construct a community. The resultant social network is a basis for managing of social relationships, discovering users with related interests, and locates content and knowledge entered by several users. We provide an actual monitoring scheme for traffic event recognition from the analysis of Twitter stream. The system was designed from ground as event-driven infrastructure, built on service oriented architecture and obtains tweets from Twitter based on various search criteria such as processes tweets, by application of text mining methods; and performs Tweet classification. The objective is to allocate the suitable class label to every tweet, as associated to traffic event or else not. The traffic detection system was in use for monitoring of numerous areas, allowing for recognition of traffic events more or less in real time, often prior to online web sites

User Information Modelling in Social Communities and Networks

User modelling is the basis for social network analysis, such as community detection, expert finding, etc. The aim of this research is to model user information including user-generated content and social ties. There have been many algorithms for community detection. However, the existing algorithms consider little about the rich hidden knowledge within communities of social networks. In this research, we propose to simultaneously discover communities and the hidden/latent knowledge within them. We focus on jointly modelling communities, user sentiment topics, and the social links. We also learn to recommend experts to the askers based on the newly posted questions in online question answering communities. Specifically, we first propose a new probabilistic model to depict users' expertise based on answers and their descriptive ability based on questions. To exploit social information in community question answering (CQA), the link analysis is also considered. We also propose a user expertise model under tags rather than the general topics. In CQA sites, it is very common that some users share the same user names. Once an ambiguous user name is recommended, it is difficult for the asker to find out the target user directly from the large scale CQA site. We propose a simple but effective method to disambiguate user names by ranking their tag-based relevance to a query question. We evaluate the proposed models and methods on real world datasets. For community discovery, our models can not only identify communities with different topic-sentiment distributions, but also achieve comparable performance. With respect to the expert recommendation in CQA, the unified modelling of user topics/tags and abilities are capable of improving the recommendation performance. Moreover, as for the user name disambiguation in CQA, the proposed method can help question askers match the ambiguous user names with the right people with high accuracy