Community detection (or clustering) in large-scale graph is an important problem in graph mining. Communities reveal interesting characteristics of a network. Louvain is an efficient sequential algorithm but fails to scale emerging large-scale data. Developing distributed-memory parallel algorithms is challenging because of inter-process communication and load-balancing issues. In this work, we design a shared memory-based algorithm using OpenMP, which shows a 4-fold speedup but is limited to available physical cores. Our second algorithm is an MPI-based parallel algorithm that scales to a moderate number of processors. We also implement a hybrid algorithm combining both. Finally, we incorporate dynamic load-balancing in our final algorithm DPLAL (Distributed Parallel Louvain Algorithm with Load-balancing). DPLAL overcomes the performance bottleneck of the previous algorithms, shows around 12-fold speedup scaling to a larger number of processors. Overall, we present the challenges, our solutions, and the empirical performance of our algorithms for several large real-world networks.

Botnets are a global problem, and effective botnet detection requires cooperation of large Internet Service Providers, allowing near global visibility of traffic that can be exploited to detect them. The global visibility comes with huge challenges, especially in the amount of data that has to be analysed. To handle such large volumes of data, a robust and effective detection method is the need of the hour and it must rely primarily on a reduced or abstracted form of data such as a graph of hosts, with the presence of an edge between two hosts if there is any data communication between them. Such an abstraction would be easy to construct and store, as very little of the packet needs to be looked at. Structured P2P command and control have been shown to be robust against targeted and random node failures, thus are ideal mechanisms for botmasters to organize and command their botnets effectively. Thus this thesis develops a scalable, efficient and robust algorithm for the detection of structured P2P botnets in large traffic graphs. It draws from the advances in the state of the art in Community Detection, which aim to partition a graph into dense communities. Popular Community Detection Algorithms with low theoretical time complexities such as Label Propagation, Infomap and Louvain Method have been implemented and compared on large LFR benchmark graphs to study their efficiency. Louvain method is found to be capable of handling graphs of millions of vertices and billions of edges. This thesis analyses the performance of this method with two objective functions, Modularity and Stability and found that neither of them are robust and general. In order to overcome the limitations of these objective functions, a third objective function proposed in the literature is considered. This objective function has previously been used in the case of Protein Interaction Networks successfully, and used in this thesis to detect structured P2P botnets for the first time. Further, the differences in the topological properties - assortativity and density, of structured P2P botnet communities and benign communities are discussed. In order to exploit these differences, a novel measure based on mean regular degree is proposed, which captures both the assortativity and the density of a graph and its properties are studied. This thesis proposes a robust and efficient algorithm that combines the use of greedy community detection and community filtering using the proposed measure mean regular degree. The proposed algorithm is tested extensively on a large number of datasets and found to be comparable in performance in most cases to an existing botnet detection algorithm called BotGrep and found to be significantly faster.

The field of Enterprise Architecture (EA) emerged as an answer to the increasing complexity in managing and aligning the business-IT relationship within enterprises. Both practitioners and academics have expressed interest in the field, with a growing number of publicized works related to EA. In an attempt to provide an outlook of the current research landscape of EA, a systematic literature review was conducted. Citation data from the Scopus (Elsevier) API were automatically extracted and analyzed. By applying the Louvain method on the collected data, 8 research communities and their topic were identified: (1) Enterprise Engineering (I & II), (2) Enterprise Architecture Management, (3) Enterprise Modelling, (4) IT Architecture, (5) Enterprise Integration, (6) Digital Transformation, and (7) Smart Cities. For each community, a summarized description with sub-community graphs as well as tables (describing the top authors, articles, and affiliation countries) are presented. Lastly, a comparison of the results and the EA trends identified by Gampfer et al. are presented. / Fältet Enterprise Architecture (EA) framkom som ett svar på den ökande komplexiteten i att hantera och anpassa affärs-IT-relationen inom företag. Både utövare och akademiker har uttryckt intresse för området, då antal publicerade verk relaterade till EA fortsätter att växa. I ett försök att ge en syn på det aktuella forskningslandskapet inom EA genomfördes ett systematisk litteraturöversikt. Citeringsdata från Scopus (Elsevier) API extraherades och analyserades automatiskt. Genom att tillämpa Louvain-metoden på insamlade datan identifierades 8 forskarsamhällen och deras ämnen: (1) Enterprise Engineering (I & II), (2) Enterprise Architecture Management, (3) Enterprise Modelling, (4) IT Architecture, (5) Enterprise Integration, (6) Digital Transformation och (7) Smart Cities. För varje gemenskap gavs en sammanfattad beskrivning med undergruppsdiagram samt tabeller (över t.ex. de främsta författarna, artiklarna, och anslutningsländerna). Slutligen så gjordes en jämförelse av resultaten och de EA trender som identifierats av Gampfer et al.