Research projects of COIN are mentioned below:

  • Multilayer Networks/ Heterogeneous Networks: While a considerable amount of research has been done on characteristics of single networks (such as Facebook and Twitter) for over a decade, real phenomena are seldom constrained inside a single network. Indeed, many real-world systems include multiple subsystems or layers of connectivity, and it is important to consider such "multilayer" perspective to improve our understanding of these systems. When only single networks are involved, it is well known that for all the dynamical processes (such as spreading and cascading failures) the structure of the network plays an important role on the outcomes of the process. For example, behavior spreading can stall when it enters a tightly-knit community within the network. The same is true when multilayer networks are involved, but the effect of the layer structures and their interdependence may differ from the single-network case. Therefore, a great deal of interest has been recently devoted to the study of multilayer networks. In summary, there are two types of multilayer networks: (i) Multiplex Networks in which the same set of nodes are across all layers and there are multiple types of relationships between them, (ii) Interconnected (Interdependent) Networks where the nodes are of different types and such networks involve multiple networks. Multilayer networks are also known as heterogeneous, multidimensional, multiple, multisliced, multilevel networks, and networks of networks. In the COIN lab, we are focused on different research problems such as (1) Analyzing, modeling and predicting information diffusion in multilayer networks, (2) Similarity search in heterogeneous networks, (3) Network formation and Evolution, (4) Network Visualization, and (5) Anomaly Detection.
  • Opinion mining and sentiment analysis: The study of diverse topics such as the propagation of opinions (or its related concepts likes sentiments) about a new product over social networks, or the spreading of a virus across different species (e.g., avian flu spreading through birds and humans), requires the development of suitable spreading models that take into consideration the existence and interactions of different layers within a network. In above research domain, we are mainly focused on the roles of structural properties of a network (i.e., the topology of a multilayer network as an underlying network) in the behavior of dynamical processes over them. Other phenomena may require another approach to be considered. For example, with rapid growth of Web 2.0 technologies and social media, nowadays we have a huge volume of opinionated data recorded in digital forms such as online reviews, forum discussions, comments on blogs and social networks. In the domain of opinion mining and sentiment analysis, we are trying to process the content of a media (e.g., a tweet on Twitter) with NLP (natural language processing) methods for behavior analysis, classification, clustering and profiling. In particular, we are focused on (1) Polarity detection in user reviews on social media sites, (2) Fake review detection, and (3) Text Similarity.
  • Crowdsourcing: In general, crowdsourcing can be considered as using a large number of human contributors (called workers) and their capabilities for solving the problems that neither machines nor humans can solve alone. Some of more prominent applications of crowdsourcing include media (e.g., Image) tagging, item categorization, sentiment analysis, data collection, (article) writing, survey, and transcription (from image, audio and video). In addition, there are a number of online marketplaces for crowdsourcing such as Amazon's Mechanical Turk and CrowdFlower. Our work in the area of crowdsourcing is focused on quality control and aggregation methods for the answers collected from crowd workers as one of the biggest challenges in this area.
  • Gamefication: Gamification is the use of game elements and game design techniques in non-game contexts to increase target behavior and engagement. Three main areas in which gamification adds value are: (1) External contexts: gamification for company's customers (things like marketing and sales which are external to the organization). (2) Internal contexts: is about applications of gamification for people within a company for example HR, productivity enhancement and crowdsourcing. (3) Behavior change: situations where someone wants to do something but can't get over the hump like health and wellness, sustainability and personal finance. Effective Gamification is benefited by a well-designed Gamified system. So, there are different kinds of frameworks which help marketers and other professionals to step by step design a Gamified system that encourages certain behavior and stimulates engagement. We particularly focused our efforts on applying gamification frameworks on different non-game contexts.