A list of projects along with their description is given below. The list is cerainly not exhaustive and other projects that comply the goals and objective of DaMiVA can be persued in consultation with the Coordinator of research group.

1. Network Analysis using Graph Metrics

There are a number of metrics available to measure characteristics of a network. Studying the behavior of real world networks using these metrics and desgining strategies to increase efficiency and performance is an interesting research area. Moreover comparative Analysis of networks from varying domains can lead to identification of interesting common patterns.

2. Anaylsis of Code Dependencies in Programming Languages

Softwares have code dependencies where methods/functions of a software demonstrate complex interaction and information interchange. These dependencies can be studied to improve the design and ameliorate the overall quality of a software. We can also use these dependency networks to predict the error/fault occurrences in a software and guide software testing process accordingly.

3. Social Network Analysis and Social Computing

With the explosion of online communities, analysis and study of these virtual communities has resulted in tremendous amout of knowledge which is now being extensively used by search engines, online shopping centers, web browsers and so on. This field of social computing has shown research potential in the recent years and promises to direct the future of the Web.

4. Clustering and Text Mining collection of Documents and Web Pages

With the current information overload, Documents in the form of Web Pages, Pdf and Doc Files create a huge mass of data. Analysis and Organization of these documents requires Text Mining, Clustering and Visual Analysis methods in order to facilitate users in searching and browsing these documents. This project aims to develop new algorithms and techniques which can help explore collection of documents efficiently.

5. Clustering Dynamic Data: Temporal Behavior of Real World Relational Data
In real world, Data is constantly changing such as in the stock markets, social networks, online transaction systems, world wide web etc. A challenging problem is to study this dynamic behavior and tackle the temporal dimension of data to extract information and hidden knowledge. There are only a few methods that propose solution to this problem and we believe that there is a lot of potentional for research in this emerging field.

6. Statistical Data Mining Techniques and Small World-Scale Free Networks
Statistical Data Mining Techniques have long been used in Data Mining. Due to the recent interest in the area of Graphs and Networks, the discovery of Small World and Scale Free properties has revolutionized the field of Network Science. Examples of such networks include social networks, metabolic networks, world wide web, food web, transportation networks, chemical r eactions, electrical circuits and so on. Readers can refer to research papers for more details.

7. Embedded Systems: Optimization of Circuits using Graph Drawing Heuristics
Collaborators: M. Mohiuddin, CoE, PAF KIET
Living in the electronic world, embedded systems can be found all around us. Optimization of  circuit layouts of Integrated Circuits (ICs) used in embedded systems is a challenging problem. This project is proposed in collaboration with the College of Engineering and focuses on graph drawing algorithms addressing classical problems such as minimizing edge crossings, lengths,  constrained placements and routing to achieve area, power and speed optimizaion of ICs with very large scale integration (VLCI).

8. Protein Interaction Networks: Modeling, Organization and Analysis of these Networks
Recent availability of Protein Sequence Data has catalyzed research in this area. The Uniprot database contains approximately 11 million protein sequences and is growing exponentially. Organizing these protein sequences, modeling and understanding the structure of these sequences is an active area of research. Protein similarity graphs are used where nodes represent individual proteins and edges represent pairwise sequence similarities between proteins. There are a number of ways to process these similarity graphs and these present researchers with a challanging problem with a wide range of applications. Readers can refer to for more details.

9. Evaluating Clustering Quality for Graph Mining Algorithms
Clustering graphs is an important research area where many researchers have introduced new algorithms, each claiming better performance and accuracy. Surprisingly, many of these algorithms use domain dependent knowledge or the presence of ground truth for evaluating cluster quality. An open area of research is to put in place a model, which can evaluate cluster quality in the absence of any prior knowledge. Readers can refer to for more details.