Complex Networks

In this paper, we study a variant of the social network
maximum influence problem and its application to intelligently
approaching individual gang members with incentives
to leave a gang. The goal is to identify individuals who
when influenced to leave gangs will propagate this action.
We study this emerging application by exploring specific
facets of the problem that must be addressed when modeling
this particular situation. We formulate a new influence
maximization variant - the “social incentive influence”
(SII) problem and study it both formally and in the context
of the law-enforcement domain. Using new techniques
from unconstrained submodular maximization, we develop
an approximation algorithm for SII and present a suite of
experimental results - including tests on real-world police
data from Chicago.

The Internet of Things vision has recently emerged as
a result of a proliferation of a large number of networked consumer electronic devices. There is a growing need to autonomously monitor power consumption of these devices. We present a self-organizing distributed algorithm for the dynamic approximation of power consumption in networked consumer electronic devices.

Security organizations often attempt to disrupt terror or insurgent networks by targeting "high value targets" (HVT's). However, there have been numerous examples that illustrate how such networks are able to quickly re-generate leadership after such an operation. Here, we introduce the notion of a "shaping" operation in which the terrorist network is first targeted for the purpose of reducing its leadership re-generation ability before targeting HVT's. We look to conduct shaping by maximizing the network-wide degree centrality through node removal. We formally define this problem and prove solving it is NP-Complete. We introduce a mixed integer-linear program that solves this problem exactly as well as a greedy heuristic for more practical use. We implement the greedy heuristic and found in examining five real-world terrorist networks that removing only 12% of nodes can increase the network-wide centrality between 17% and 45%. We also show our algorithm can scale to large social networks of 1,133 nodes and 5,541 edges on commodity hardware.

In Social Network Analysis (SNA), community structure is an important feature of complex network. There are many researches on detecting community or cluster in graph with the objective to understand functional properties and community structures. Community detection early researches require global knowledge of network, which is not realistic to most real world network. Due to the increase of online social network, the new challenges are to develop methods to support community detection based on local information-only and network modularity. This paper present state of the art of methods in community detection research and propose the direction of future community detection research.

The present volume seeks to indicate novel methods and approaches to analysing and interpreting the agency of individual officials in different periods of ancient Egyptian history. Their activity and careers are observed using different methods of complex network analysis and put into a broader framework of more general trends operating the society at a given stage of its evolution. We are confident that this is one of the most promising and proven ways to gain deeper insights into day-to-day lives of the people of the past. The story of civilisations is above all a story of ideas and thoughts, and social/complex network analysis is one of the most efficient tools we can use. It enables us to view known data anew and assess them from new perspectives that significantly expand and deepen our knowledge of the past civilisation.
In recent years, this research approach has evolved independently at several institutions exploring ancient Egypt. We were very pleased to host most of these scholars at a joint meeting and offer them an opportunity to present and communicate their individual approaches, methods, points of view and observations. The contributions in this volume, originally presented at a workshop in Prague in September 2018, cover selected periods of ancient Egypt (the Old Kingdom, the New Kingdom, the Greco-Roman Period). Cyber-Egyptology, a new area of research in Egyptology, appears to be a justified approach with its own methodology, philosophy and a vast potential to answer complex questions relating to this fascinating civilisation and its diachronic dynamics. Moreover, this method of cyber-research can be applied universally across most archaeological and historical specialisations.

""In more than 1000 years of history, the Byzantine Empire experienced several severe times of crisis which brought it almost to the point of destruction. Yet, Byzantium proved to be one of the most resilient polities of medieval Europe and endured; even after the loss of its capital to the Crusaders of 1204, Byzantine statehood and church were able to regenerate in exile and to reclaim Constantinople after 57 years. But the political and economic environment had changed dramatically, and Byzantium could not re-establish its own imperial sphere in the Eastern Mediterranean; the commercial centres of Italy
(Venice, Genoa) had integrated Byzantium´s former territories in the late medieval “Worldsystem” , in which Byzantium only occupied a position at the periphery; and new expansive Turkish polities had emerged in Western Asia Minor, which reduced Byzantium to a South-Eastern European regional power
and finally after 1350 extended their power to the politically fragmented Balkans. Internally, competing aristocratic factions, ecclesiastical disputes and a “lack of unity and social cohesion” weakened the central state´s ability to adapt to the challenges of this new environment. Despite this complex of factors and
developments, contemporary scholarship still often considers Late Byzantium a “Pseudo-Empire”, more or less “programmed” for destruction after 1204 (or even earlier) , and interprets the development of these 250 years from the perspective of its endpoint – the Ottoman conquest of 1453. Our paper challenges this view; it aims at a new analysis of Byzantium´s “last centuries” not as an isolated case, but from the perspective of a pre-modern polity facing the same dramatic changes and challenges as others societies did at the same time of the “Late Medieval Crisis”, which took hold of the entire old world from China to England in the 14th century. At the same time, we implement concepts, models and tools provided by the
new fields of complexity studies and social network analysis in order to include the historical dynamics of crisis and adaptation in all its complexities at the level of macro-processes (in demography, climate and economy), of the structural framework of political, economic, social and religious networks, of individual
and collective decision making and reaction to crisis phenomena. Thus, it becomes possible to identify similarities and peculiarities of Byzantium´s development in comparison with other contemporary polities and to find answers to the question why some segments of the Byzantine framework were able to adapt and to survive beyond 1453 within the new Ottoman framework while the Byzantine polity in its totality
collapsed.""

In Social Computing, Social Network Analysis (SNA) provides models and techniques for analysing social and economic network based on graph theory. SNA can help us to understand the real-world network application such as knowledge management, market segmentation, viral marketing, customer behavior, competitive advantage and many other applications.  The ability to quantify complex network can greatly give an advantage for decision support. There are three approaches in current SNA study: Graph Representation, Content Mining, and Semantic Analysis. In this paper, we focus on graph representation approach which has been used for analyzing social network topology, structural modeling, tie-strength, community detection, group cohesion, visualization, and metrics computations. Many research contribute to the development of SNA, they are based from various idea and sometimes is difficult to track the development of this field. This paper provides SNA taxonomy based on its graph representation