Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications.

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A rapidly rising number of civilian and military realworld applications require deployments of large sensor networks. However, problems like limited energy supply, tough environments, data latency and integrity cause adverse effects on large topologies of sensors. This paper presents a novel approach in designing the placement of relay nodes in a sensor network. By using concepts from the area of social network analysis and mapping them to the already classical field of sensor networks we succeed to add improvements to the costs implied with deploying the infrastructure. By socializing the topology with the concepts of centrality and community structure, our research is focused around a flexible design space exploration algorithm we have devised, which offers a balance between the performance and cost of deploying relays in a sensor network. As a result, our WSN design achieves a relevant improvement over the state of the art solutions.Index Terms-wireless sensor network topology, social network analysis, design space exploration algorithm.