A General Optimization Technique for High Quality Community Detection in Complex Networks

Recent years have witnessed the development of a large body of algorithms for community detection in complex networks. Most of them are based upon the optimization of objective functions, among which modularity is the most common, though a number of alternatives have been suggested in the scientific literature. We present here an effective general search strategy for the optimization of various objective functions for community detection purposes. When applied to modularity, on both real-world and synthetic networks, our search strategy substantially outperforms the best existing algorithms in terms of final scores of the objective function; for description length, its performance is on par with the original Infomap algorithm. The execution time of our algorithm is on par with non-greedy alternatives present in literature, and networks of up to 10,000 nodes can be analyzed in time spans ranging from minutes to a few hours on average workstations, making our approach readily applicable to tasks which require the quality of partitioning to be as high as possible, and are not limited by strict time constraints. Finally, based on the most effective of the available optimization techniques, we compare the performance of modularity and code length as objective functions, in terms of the quality of the partitions one can achieve by optimizing them. To this end, we evaluated the ability of each objective function to reconstruct the underlying structure of a large set of synthetic and real-world networks.Comment: MAIN text: 14 pages, 4 figures, 1 table Supplementary information: 19 pages, 8 figures, 5 table

Identifying communities by influence dynamics in social networks

Communities are not static; they evolve, split and merge, appear and disappear, i.e. they are product of dynamical processes that govern the evolution of the network. A good algorithm for community detection should not only quantify the topology of the network, but incorporate the dynamical processes that take place on the network. We present a novel algorithm for community detection that combines network structure with processes that support creation and/or evolution of communities. The algorithm does not embrace the universal approach but instead tries to focus on social networks and model dynamic social interactions that occur on those networks. It identifies leaders, and communities that form around those leaders. It naturally supports overlapping communities by associating each node with a membership vector that describes node's involvement in each community. This way, in addition to overlapping communities, we can identify nodes that are good followers to their leader, and also nodes with no clear community involvement that serve as a proxy between several communities and are equally as important. We run the algorithm for several real social networks which we believe represent a good fraction of the wide body of social networks and discuss the results including other possible applications.Comment: 10 pages, 6 figure

Molecular Analysis of Microbial Communities in Endotracheal Tube Biofilms

Ventilator-associated pneumonia is the most prevalent acquired infection of patients on intensive care units and is associated with considerable morbidity and mortality. Evidence suggests that an improved understanding of the composition of the biofilm communities that form on endotracheal tubes may result in the development of improved preventative strategies for ventilator-associated pneumonia. (n = 5). DGGE profiling of the endotracheal biofilms revealed complex banding patterns containing between 3 and 22 (mean 6) bands per tube, thus demonstrating the marked complexity of the constituent biofilms. Significant inter-patient diversity was evident. The number of DGGE bands detected was not related to total viable microbial counts or the duration of intubation.Molecular profiling using DGGE demonstrated considerable biofilm compositional complexity and inter-patient diversity and provides a rapid method for the further study of biofilm composition in longitudinal and interventional studies. The presence of oral microorganisms in endotracheal tube biofilms suggests that these may be important in biofilm development and may provide a therapeutic target for the prevention of ventilator-associated pneumonia

Research in progress: report on the ICAIL 2017 doctoral consortium

This paper arose out of the 2017 international conference on AI and law doctoral consortium. There were five students who presented their Ph.D. work, and each of them has contributed a section to this paper. The paper offers a view of what topics are currently engaging students, and shows the diversity of their interests and influences

Geographic Visualization in Archaeology

Archaeologists are often considered frontrunners in employing spatial approaches within the social sciences and humanities, including geospatial technologies such as geographic information systems (GIS) that are now routinely used in archaeology. Since the late 1980s, GIS has mainly been used to support data collection and management as well as spatial analysis and modeling. While fruitful, these efforts have arguably neglected the potential contribution of advanced visualization methods to the generation of broader archaeological knowledge. This paper reviews the use of GIS in archaeology from a geographic visualization (geovisual) perspective and examines how these methods can broaden the scope of archaeological research in an era of more user-friendly cyber-infrastructures. Like most computational databases, GIS do not easily support temporal data. This limitation is particularly problematic in archaeology because processes and events are best understood in space and time. To deal with such shortcomings in existing tools, archaeologists often end up having to reduce the diversity and complexity of archaeological phenomena. Recent developments in geographic visualization begin to address some of these issues, and are pertinent in the globalized world as archaeologists amass vast new bodies of geo-referenced information and work towards integrating them with traditional archaeological data. Greater effort in developing geovisualization and geovisual analytics appropriate for archaeological data can create opportunities to visualize, navigate and assess different sources of information within the larger archaeological community, thus enhancing possibilities for collaborative research and new forms of critical inquiry