The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

/pdf/community-detection-in-graphs-34rogm3r6p.pdf

Community detection in graphs

/pdf/introduction-to-social-network-methods-36g7099hce.pdf

Introduction to Social Network Methods

Complex networks arise in a wide range of biological and sociotechnical systems. Epidemic spreading is central to our understanding of dynamical processes in complex networks, and is of interest to physicists, mathematicians, epidemiologists, and computer and social scientists. This review presents the main results and paradigmatic models in infectious disease modeling and generalized social contagion processes.

Epidemic processes in complex networks

Spreading of information, ideas or diseases can be conveniently modelled in the context of complex networks. An analysis now reveals that the most efficient spreaders are not always necessarily the most connected agents in a network. Instead, the position of an agent relative to the hierarchical topological organization of the network might be as important as its connectivity.

/pdf/identification-of-influential-spreaders-in-complex-networks-4zv30jmeio.pdf

Identification of influential spreaders in complex networks

After four decades of research on a broad range of topics, computing education has now emerged as a mature research community, with its own journals, conferences, and monographs. Despite this success, the computing education research community still lacks a commonly recognized core literature. A core literature can help a research community to develop a common orientation and make it easier for new researchers to enter the community. This paper proposes an approach to constructing and maintaining a core literature for computing education research. It includes a model for classifying research contributions and a methodology for determining whether they should be included in the core. The model and methodology have been applied to produce an initial list of core papers. An annotated list of these papers is given in appendix A.

Constructing a core literature for computing education research

Structures induced by collections of subsets: a hypergraph approach

Internal cohesion of ls sets in graphs

A model is proposed that formalizes the design of hierarchical module structures. The model is specified by a collection of Z schema type definitions that is invariant across all applications. A particular application is described by specifying the values of generic parameters and adding application-specific declarations and constraints to the schema definitions. As applications, the definitions in the model are used to describe the Conic configuration language and the STILE graphical design and development environment. >

A formal model for module interconnection languages

Software Engineering graduates are expected to enter the workforce with both technical and soft skills. In addition, software quality is a topic that is becoming increasingly important both because of educational and industry requirements. Software engineering lecturers need to bring their research into the classroom. Bringing all of these together can pose the lecturer with a dilemma that is not easily solvable. This paper presents how problem-based learning, a pedagogical methodology that is popular in medicine and other disciplines, can be used to accomplish these goals in a single course module. It describes a research project which analyses the implementation of problem-based learning within a M. Sc. Software Engineering Quality Module, and evaluates the outcomes against published expectations.

https://ulir.ul.ie/bitstream/handle/10344/1710/2011_Richardson.pdf?sequence=2

Stephen B. Seidman

Papers

Constructing a core literature for computing education research

Structures induced by collections of subsets: a hypergraph approach

Internal cohesion of ls sets in graphs

A formal model for module interconnection languages

Educating software engineers of the future: Software quality research through problem-based learning