Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust’s Social Network Analysis: Methods and Applications, it is a collection of original articles by leading methodologists reviewing recent advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodeling, the analysis of diffusion through networks, the analysis of affiliation or “two-mode” networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphic techniques for exploring network data, and software for the analysis of social networks.

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Models and methods in social network analysis

Being able to identify the groups of clients that are responsible for a significant portion of a Web site's requests can be helpful to both the Web site and the clients. In a Web application, it is beneficial to move content closer to groups of clients that are responsible for large subsets of requests to an origin server. We introduce clusters---a grouping of clients that are close together topologically and likely to be under common administrative control. We identify clusters using a ``network-aware" method, based on information available from BGP routing table snapshots.

On Network-Aware Clustering of Web Clients

Social network security: Issues, challenges, threats, and solutions

A very important problem in combinatorial optimization is the partitioning of a network into communities of densely connected nodes; where the connectivity between nodes inside a particular community is large compared to the connectivity between nodes belonging to different ones. This problem is known as community detection, and has become very important in various fields of science including chemistry, biology and social sciences. The problem of community detection is a twofold problem that consists of determining the number of communities and, at the same time, finding those communities. This drastically increases the solution space for heuristics to work on, compared to traditional graph partitioning problems. In many of the scientific domains in which graphs are used, there is the need to have the ability to partition a graph into communities with the "highest quality" possible since the presence of even small isolated communities can become crucial to explain a particular phenomenon. We have explored community detection using the power of quantum annealers, and in particular the D-Wave 2X and 2000Q machines. It turns out that the problem of detecting at most two communities naturally fits into the architecture of a quantum annealer with almost no need of reformulation. This paper addresses a systematic study of detecting two or more communities in a network using a quantum annealer.

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Detecting multiple communities using quantum annealing on the D-Wave system.

Large graph processing has gained great attention in recent years due to its broad applicability from machine learning to social science. Large real-world graphs, however, are inherently difficult to process efficiently, not only due to their large memory footprint, but also that most graph algorithms entail memory access patterns with poor locality and a low compute-to-memory access ratio. In this work, we leverage the exceptional random access performance of emerging Hybrid Memory Cube (HMC) technology that stacks multiple DRAM dies on top of a logic layer, combined with the flexibility and efficiency of FPGA to address these challenges. To our best knowledge, this is the first work that implements a graph processing system on a FPGA-HMC platform based on software/hardware co-design and co-optimization. We first present the modifications of algorithm and a platform-aware graph processing architecture to perform level-synchronized breadth first search (BFS) on FPGA-HMC platform. To gain better insights into the potential bottlenecks of proposed implementation, we develop an analytical performance model to quantitatively evaluate the HMC access latency and corresponding BFS performance. Based on the analysis, we propose a two-level bitmap scheme to further reduce memory access and perform optimization on key design parameters (e.g. memory access granularity). Finally, we evaluate the performance of our BFS implementation using the AC-510 development kit from Micron. We achieved 166 million edges traversed per second (MTEPS) using GRAPH500 benchmark on a random graph with a scale of 25 and an edge factor of 16, which significantly outperforms CPU and other FPGA-based large graph processors.

Boosting the Performance of FPGA-based Graph Processor using Hybrid Memory Cube: A Case for Breadth First Search

Web 2.0 has led to the expansion and evolution of web-based communities that enable people to share information and communicate on shared platforms. The inclination of individuals towards other individuals of similar choices, decisions and preferences to get related in a social network prompts the development of groups or communities. The identification of community structure is one of the most challenging task that has received a lot of attention from the researchers. Network community structure detection can be expressed as an optimisation problem. The objective function selected captures the instinct of a community as a group of nodes in which intra-group connections are much denser than inter-group connections. However, this problem often cannot be well solved by traditional optimisation methods due to the inherent complexity of network structure. Therefore, evolutionary algorithms have been embraced to deal with community detection problem. Many objective functions have been proposed to capture the notion of quality of a network community. In this paper, we assessed the performance of four important objective functions namely Modularity, Modularity Density, Community Score and Community Fitness on real-world benchmark networks, using Genetic Algorithm (GA). The performance measure taken to assess the quality of partitions is NMI (Normalized mutual information). From the experimental results, we found that the communities’ identified by these objectives have different characteristics and modularity density outperformed the other three objective functions by uncovering the true community structure of the networks. The experimental results provide a direction to researchers on choosing an objective function to measure the quality of community structure in various domains like social networks, biological networks, information and technological networks.

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Comparative analysis of quality metrics for community detection in social networks using genetic algorithm

Abusive Content Detection in Online User-Generated Data: A survey

computing is a form of distributed computing that provides a platform for executing large-scale resource intensive applications on a number of heterogeneous computing systems across multiple administrative domains. Therefore, Grid platforms enable sharing, exchange, discovery, selection, and aggregation of distributed heterogeneous resources such as computers, databases and visualization devices. Job and resource scheduling is one of the key research area in grid computing. In a grid computing environment, a scheduler is responsible for selecting the best suitable computing resources in the grid for processing jobs to achieve high system throughput. Further, grouping the fine grained jobs according to the processing capability of available resources results in better throughput, resource utilization and low communication time. Motivation of this study is to encourage and help the amateur researcher in the field of grid computing, so that they can understand easily the concept of scheduling, job grouping and can contribute in developing more efficient and practical scheduling algorithm. In this paper, we compared three job grouping based scheduling algorithms that will benefit interested researchers to carry out further work in this thrust area of research.

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Comparative Analysis of Job grouping based Scheduling Strategies in Grid Computing

A Look Forward and a Look Back: The Growing Role of ERAS Protocols in Orthopedic Surgery.

Data mining is defined as the process in which useful information is extracted from the raw data. In order to acquire essential knowledge it is essential to extract large amount of data. This process of extraction is also known as misnomer. Currently in every field, large amount of data is present and analyzing whole data is very difficult as well as it consumes a lot of time. The prediction analysis is most useful type of data which is performed today. To perform the prediction analysis the patterns needs to generate from the dataset with the machine learning. The prediction analysis can be done by gathering historical information to generate future trends. So, the knowledge of what has happened previously is used to provide the best valuation of what will happen in future with predictive analysis. Crop production analysis is one of the applications of prediction analysis. In this research work, the Naïve Bayes classifier is applied for the wheat production prediction. The Naïve Bayes classifier is compared with SVM and KNN. The Naïve Bayes performs well for the wheat production analysis.

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Simrat Kaur

Papers

Comparative analysis of quality metrics for community detection in social networks using genetic algorithm

Abusive Content Detection in Online User-Generated Data: A survey

Comparative Analysis of Job grouping based Scheduling Strategies in Grid Computing

A Look Forward and a Look Back: The Growing Role of ERAS Protocols in Orthopedic Surgery.

Analysis of Wheat Production using Naïve Bayes Classifier