We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Social Network Analysis

Social network based applications have experienced exponential growth in recent years. One of the reasons for this rise is that this application domain offers a particularly fertile place to test and develop the most advanced computational techniques to extract valuable information from the Web. The main contribution of this work is three-fold: (1) we provide an up-to-date literature review of the state of the art on social network analysis (SNA);(2) we propose a set of new metrics based on four essential features (or dimensions) in SNA; (3) finally, we provide a quantitative analysis of a set of popular SNA tools and frameworks. We have also performed a scientometric study to detect the most active research areas and application domains in this area. This work proposes the definition of four different dimensions, namely Pattern & Knowledge discovery, Information Fusion & Integration, Scalability, and Visualization, which are used to define a set of new metrics (termed degrees) in order to evaluate the different software tools and frameworks of SNA (a set of 20 SNA-software tools are analyzed and ranked following previous metrics). These dimensions, together with the defined degrees, allow evaluating and measure the maturity of social network technologies, looking for both a quantitative assessment of them, as to shed light to the challenges and future trends in this active area.

The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools

The influence maximization problem has gained increasing attention in recent years. Previous research focuses on the development of algorithms to analyze static social networks. However, real social networks are not static but they are represented as dynamic networks that evolve across time. Motivated by this drawback, the purpose of this survey is to highlight the characteristics and challenges of the influential nodes detection problem. A classification of published approaches should be proposed. This work is organizing state-of-the-art methods into a technical comparison that are based on network models. Due to the definition of network models and the influential nodes detection problem, this survey will help researchers to find the set of methods best suited for their needs. The proposed classification could also help researchers to select the right direction in which their future research should be oriented.

Influential nodes detection in dynamic social networks: A survey

Encyclopedia of social network analysis and mining

https://hal.archives-ouvertes.fr/hal-01761341/document

OLCPM: An online framework for detecting overlapping communities in dynamic social networks

Many algorithms have been proposed in the last ten years for the discovery of dynamic communities. However, these methods are seldom compared between themselves. In this article, we propose a generator of dynamic graphs with planted evolving community structure, as a benchmark to compare and evaluate such algorithms. Unlike previously proposed benchmarks, it is able to specify any desired evolving community structure through a descriptive language, and then to generate the corresponding progressively evolving network. We empirically evaluate six existing algorithms for dynamic community detection in terms of instantaneous and longitudinal similarity with the planted ground truth, smoothness of dynamic partitions, and scalability. We notably observe different types of weaknesses depending on their approach to ensure smoothness, namely Glitches, Oversimplification and Identity loss. Although no method arises as a clear winner, we observe clear differences between methods, and we identified the fastest, those yielding the most smoothed or the most accurate solutions at each step.

https://hal.archives-ouvertes.fr/hal-03173685/document

Evaluating community detection algorithms for progressively evolving graphs

Evaluating Community Detection Algorithms for Progressively Evolving Graphs

Recently, a lot of research focuses on knowledge management and reuse. Pertinent reuse can facilitate learning, knowledge creation and sharing. In this research, we focus on the knowledge capitalization and reuse within Communities of Practice of E-learning (CoPEs). These communities are a virtual framework for exchanging and sharing techno-pedagogical knowledge and know-how between e-learning actors. In this chapter, we propose and discuss a knowledge capitalization approach for knowledge reuse within a CoPE. Our approach is based on contextual semantic annotations to model CoPEs members’ tacit and explicit knowledge. The context dimension represents the situation in which the members create or reuse annotations. To illustrate our approach, we have developed a prototype of knowledge capitalization system based on contextual semantic annotations, called CoPEAnnot. Ontological and rule-based context reasoning have been used to improve knowledge reuse by adapting CoPEAnnot features according to the current activity context of members. Preliminary tests and experimentation of CoPEAnnot conducted within a CoPE made up of members from the Algerian Higher National School of Computer Science show advantages and benefits.

Using an Ontological and Rule-Based Approach for Contextual Semantic Annotations in Online Communities

Link streams model interactions over time in a wide range of fields. Under this model, the challenge is to mine efficiently both temporal and topological structures. Community detection and change point detection are one of the most powerful tools to analyze such evolving interactions. In this paper, we build on both to detect stable community structures by identifying change points within meaningful communities. Unlike existing dynamic community detection algorithms, the proposed method is able to discover stable communities efficiently at multiple temporal scales. We test the effectiveness of our method on synthetic networks, and on high-resolution time-varying networks of contacts drawn from real social networks.

Souâad Boudebza

Papers

OLCPM: An online framework for detecting overlapping communities in dynamic social networks

Evaluating community detection algorithms for progressively evolving graphs

Evaluating Community Detection Algorithms for Progressively Evolving Graphs

Using an Ontological and Rule-Based Approach for Contextual Semantic Annotations in Online Communities

Detecting Stable Communities in Link Streams at Multiple Temporal Scales