Detecting Stable Communities In Large Scale Networks

A network is said to exhibit community structure if the nodes of the network can be easily grouped into groups of nodes, such that each group is densely connected internally but sparsely connected with other groups. Most real world networks exhibit community structure. A popular technique for detecting communities is based on computing the modularity of the network. Modularity reflects how well the vertices in a group are connected as opposed to being randomly connected. We propose a parallel algorithm for detecting modularity in large networks. However, all modularity based algorithms for detecting community structure are affected by the order in which the vertices in the network are processed. Therefore, detecting communities in real world graphs becomes increasingly difficult. We introduce the concept of stable community, that is, a group of vertices that are always partitioned to the same community independent of the vertex perturbations to the input. We develop a preprocessing step that identifies stable communities and empirically show that the number of stable communities in a network affects the range of modularity values obtained. In particular, stable communities can also help determine strong communities in the network. Modularity is a widely accepted metric for measuring the quality of a partition identified by various community detection algorithms. However, a growing number of researchers have started to explore the limitations of modularity maximization such as resolution limit, degeneracy of solutions and asymptotic growth of the modularity value for detecting communities. In order to address these issues we propose a novel vertex-level metric called permanence. We show that our metric permanence as compared to other standard metrics such as modularity, conductance and cut-ratio performs as a better community scoring function for evaluating the detected community structures from both synthetic networks and real-world networks. We demonstarte that maximizing permanence results in communities that match the ground-truth structure of networks more accurately than modularity based and other approaches. Finally, we demonstrate how maximizing permanence overcomes limitations associated with modularity maximization

A shared-memory algorithm for updating single-source shortest paths in large weighted dynamic networks

In the last decade growth of social media, increased the interest of network algorithms for analyzing large-scale complex systems. The networks are highly unstructured and exhibit poor locality, which has been a challenge for developing scalable parallel algorithms. The state-of-the-art network algorithms such as Prim\u27s algorithm for Minimum Spanning Tree, Dijkstra\u27s algorithm for Single Source Shortest Path and ISPAN algorithm for detecting strongly connected components are designed and optimized for static networks. The networks which change with time i.e. the dynamic networks such as social networks, the above-mentioned approaches can only be utilized if they are recomputed from scratch each time. Performing a re-computation from scratch for a significant amount of changes is not only computationally expensive, however, increases the memory footprint and the execution time. In the case of dynamic networks, developing scalable parallel algorithms is very challenging and there has been a very limited amount of research work that has been performed when compared to developing parallel scalable algorithms for static networks. To address the above challenges, this presentation proposes a new high performance, scalable, portable, open source software package and an efficient network data structure to update the dynamic networks on the fly. This approach is different from the naive approach which is the re-computation from scratch and is scalable for random, small-world, scale-free, real-world and synthetic networks. The software package currently is implemented on a shared memory system and updates network properties such as Connected Components (CC), Minimum Spanning Tree (MST), Single Source Shortest Path (SSSP), and Strongly Connected Components(SCC). The key attributes of software are faster insertions, and deletions when Comparing the software with the state-of-the-art network algorithms package such as Galois for MST takes less time and memory for updating the network. The shared memory implementation processes over 50 million updates on a real-world network under 30 seconds. The dissertation concludes with a summarization of the contributions and their improvement on large-scale network analytics and a discussion about future work on this field

An Enhanced Security Model for Protecting Data Transmission and Communication in Recent IoT Integrated Healthcare Industry Using Machine Learning Algorithm

Different kinds of security need to be applied to various application-centric IoT networks. Safety is one of the most important aspects to be considered regarding user, device, and data. The healthcare industry is a special IoT network fully connected with medical/healthcare IoT devices. The data generated from the IoT devices are transmitted or shared from one hospital to another through the Internet. Healthcare data has more private, medical, and insurance information that intruders can use on the Internet. The intruders misbehave with the patient or the general public registered in the healthcare industry. Some intruders blackmail the patient based on their private/personal information. Healthcare industries and their research team are trying to create a security framework to safeguard the data to avoid these malicious activities. This paper aims to secure and analyze healthcare IoT data using the Support Vector Machine algorithm. It learns the entire dataset, classifies it, and calls the encryption-decryption algorithms (RSA) to secure private data. The proposed SVM and the RSA algorithm are implemented in Python, and the results are verified. The performance of the proposed SVM-RSA is evaluated by comparing its results with the other algorithms

On the Permanence of Vertices in Network Communities

Despite the prevalence of community detection algorithms, relatively less work has been done on understanding whether a network is indeed modular and how resilient the community structure is under perturbations. To address this issue, we propose a new vertex-based metric called "permanence", that can quantitatively give an estimate of the community-like structure of the network. The central idea of permanence is based on the observation that the strength of membership of a vertex to a community depends upon the following two factors: (i) the distribution of external connectivity of the vertex to individual communities and not the total external connectivity, and (ii) the strength of its internal connectivity and not just the total internal edges. In this paper, we demonstrate that compared to other metrics, permanence provides (i) a more accurate estimate of a derived community structure to the ground-truth community and (ii) is more sensitive to perturbations in the network. As a by-product of this study, we have also developed a community detection algorithm based on maximizing permanence. For a modular network structure, the results of our algorithm match well with ground-truth communities.Comment: 10 pages, 5 figures, 8 tables, Accepted in 20th ACM SIGKDD Conference on Knowledge Discovery and Data Minin