12 May

Nandor Poka, (research assistant (Department of Applied Informatics) and doctoral candidate (Doctoral School of Multidisciplinary Medical Science) University of Szeged): Combinatorial Scientific Computing and (Computational) Biology: When the demand meets the offer

Abstract: With the rise of the BigData and High-Performance Computing era, came the flourishing of many fields of science, including Combinatorial Scientific Computing. Both the business and scientific community produces such vast amounts of data, that new technologies and algorithms are required to analyze them. To efficiently utilize highly parallel computers or clusters, tasks must be decomposed and the data must be partitioned, and these involve graph algorithms themselves. Combinatorial Scientific Computing methods have been used for eg. in the aforementioned load distribution, automatic differentiation, statistical physics, and other enigmatic areas. However more “life-like” applications of these methods were and are put to use in other fields of science like modern biology. In our talk we will present various biological problems (both “old” and more recent) that can be fairly easily represented with mathematical objects such as graphs and matrices. Our main focus of these problems will be the present and future challenges of Next-Generation Sequencing, and how to tackle them using Data Science and Combinatorial Scientific Computing.

28 April

Miklos Kresz (Department of Applied Informatics, University of Szeged; joint work with Andras Bota and Andras Pluhar): Dynamic network mining and business intelligence

Abstract: During the last decade social network analysis and mining became a key research area. Apart from the obvious applications in on-line social network services, the field plays central role in many classical business intelligence tasks such as customer attrition, risk analysis and campaign management. In order to capture the characteristics of the above problems, the dynamics of the corresponding network processes and that of the changes in the network structure needs to be studied. In this talk we will consider two relevant problems. Dynamic community detection is an algorithmic tool for the analysis of the lifetime of communities in real graphs. The study of infection processes in networks pose several algorithmic and modelling questions such as maximizing the spread of influence or approximating the real infection values. In addition to review applied models and methods, in the talk real-life applications will be also presented.

7 April

Bogdan Zavalnij(University of Pecs): Mathematical modeling of various problems by graphs and solving them with clique search

Abstract: In our talk we would like to demonstrate the modeling powers of graphs. We will demonstrate problems from different fields and show that they can be reformulated as graphs. The solution of these problems will be transformed to usual graph problems as different coloring and clique problems. The problems included in the talk will be from simple games, scheduling, stock exchange, drug design and even graph problems themselves -- including hypergraph coloring problems. We would like to show some useful techniques and also some common methods for such reformulations that could prove useful.

24 March

Christian Bongirono (University of Palermo): The dual-projection approach for bipartite networks using statistical link validation

Abstract: Bipartite complex networks are usually analyzed by projecting the two disjoint set of nodes into two networks and then using the standard techniques for them separately. Since complex systems are often very heterogeneous that makes very difficult to distinguish links of the projected network that are just reflecting system’s heterogeneity from links relevant to unveil the properties of the system. To avoid this problem it has been developed a methodology for one-mode projections of bipartite networks using an unsupervised statistical link validation. In order to study the efficiency of the method we investigate the community structure of the projected network using both a simple projection and the statistically validated projection on various synthetic benchmarks and real networks. In all these cases the

link validating filtering procedure necessarily increases the precision and suggested to use, even if considering the drawback that it decreases the level of accuracy in certain situations.

10 March

Christian Bongirono (University of Palermo): Statistical Regularity in the Air Traffic flow

Abstract: The aircraft trajectories are compounded by a sequence of spatial fixed points (NVP) that typically diverge from the best path route. This infrastructure allows the air traffic controllers to direct the air traffic flow on standard air ways, and focuses their attention to a few numbers of special NVPs where the routes converge. As a drawback the not optimal routes force the air traffic controller to modify, where is possible, the routes to enhance the air traffic flow. The aim of the first part of the talk is to highlight the behaviour of the air traffic controller respect to a network optimization operation named direct by the observation of stylized facts both at the global trajectory level and at the local navigation point level. In the second part of the talk will be discussed how an Agent Based Model could help us in understanding the transition from the current NVP based network to the future new SESAR scenario, where the aircraft will be allowed to follow a free-route path.