searchfortruth.info > Criminal | Social Networks

Criminal | Social Networks

Organizing for Resistance: How Group Structure Impacts the Character of Violence

February 20, 2019September 23, 2017 by admin

Introduction

Between the beginning of the al-Aqsa intifada in September 2000 and March 22, 2004, the Israeli Ministry of Foreign Affairs reports that Hamas was responsible for a lethal wave of approximately 425 attacks against Israeli citizens and their interests, resulting in nearly 2500 injured or killed.1 It is remarkable that a single organization had the organizational capacity to perpetrate such a concerted, sustained wave of attacks. Indeed, over the course of the four years, attacks occurred nearly every four days and, on average, harmed more than five individuals each time. This is contrasted with the more grassroots Swords of Truth, a militant, religious Palestinian group that detonated bombs at nightclubs that caused damage, but no injuries.2 How does a violent non-state group like Hamas coordinate to achieve these types of ends? More specifically, what factors make it possible for a group such as Hamas to carry out such damaging campaigns? ...

Who’s Who in Crime Networks. Wanted: The Key Player

February 12, 2019July 8, 2017 by admin

Abstract

Criminals are embedded in a network of relationships. Social ties among criminals are modeled by means of a graph where criminals compete for a booty and benefit from local interactions with their neighbors. Each criminal decides in a non-cooperative way how much crime effort he will exert. We show that the Nash equilibrium crime effort of each individual is proportional to his equilibrium Bonacich-centrality in the network, thus establishing a bridge to the sociology literature on social networks. We then analyze a policy that consists of finding and getting rid of the key player, that is, the criminal who, once removed, leads to the maximum reduction in aggregate crime. We provide a geometric characterization of the key player identified with an optimal inter-centrality measure, which takes into account both a player’s centrality and his contribution to the centrality of the others. We also provide a geometric characterization of the key group, which generalizes the key player for a group of criminals of a given size....

Gangs And The Structure Of U.S. Society

February 12, 2019July 7, 2017 by admin

Abstract

Extant literature on the subject has usually defined gangs as loose associations of individuals engaged in some type of delinquent or criminal activity. Yet researchers have failed to sociologically differentiate gangs from other types of collective behavior. In contrast, this article understands gangs as organizations influenced by the social structure of the urban areas in which they operate. Concentrating on gangs in the US context, the article summarizes both common features and different forms gangs have assumed over five historical eras, arguing that gangs respond to rather than create significant social changes.

Additional Resource: Gangs and Social Change

Event Classification and Relationship Labeling in Aliation Networks

February 11, 2019July 6, 2017 by admin

Abstract

Many domains are best described as an affiliation network in which there are entities such as actors, events and organizations linked together in a variety of relationships. Relational classification in these domains requires the collective classification of both entities and relationships. In this paper, we investigate the use of relational Markov networks (RMN) for relational classification in affiliation networks. We study two tasks, event classification and relationship labeling, and discuss general issues in constructing an appropriate RMN from an affiliation network. We evaluate our methods on a novel dataset describing terrorist affiliation networks which includes data about actors (terrorists), events (terrorist attacks) and organizations (terrorist organizations). Our results highlight several important issues concerning the effectiveness of relational classification and our experiments show that the relational structure significantly helps relationship labeling.

1. Introduction
Traditional machine learning techniques mainly concentrate on identically and independently distributed samples. However, most real-world datasets are relational in nature and the correlations due to the link structure provide an important source of information.

Emergence of a Small World from Local Interactions: Modeling Acquaintance Networks

February 11, 2019July 6, 2017 by admin

A remarkable feature of many complex systems is the occurrence of large and stable network structures as, for example, networks on the protein or gene level, ecological webs, communication networks, and social networks[1 – 3]. Simple models based on disordered networks are quite successful in describing basic properties of such systems. When addressing topological properties, however,neither random networks nor regular lattices provide an adequate framework. A helpful concept along this line is the idea of “small-world networks” introduced by Watts and Strogatz [4,5] which initiated an avalanche of scientific activity in this field [6 – 11]. Small-world networks interpolate between the two limiting cases of regular lattices with high local clustering and of random graphs with short distances between nodes. High clustering implies that, if node A is linked to node B, and B is linked to node C, there is an increased probability that A will also be linked to C. Another useful measure is the distance between two nodes, defined as the number of edges along the shortest path connecting them. A network is called a “small-world network” if it exhibits the following two characteristic properties [4,12]: (i) high cluster in...

Strong and Weak Ties

February 11, 2019July 6, 2017 by admin

One of the powerful roles that networks play is to bridge the local and the global offer explanations for how simple processes at the level of individual nodes and links can have complex effects that ripple through a population as a whole. In this chapter, we consider some fundamental social network issues that illustrate this theme: how information flows through a social network, how different nodes can play structurally distinct roles in this process, and how these structural considerations shape the evolution of the network itself over time. These themes all play central roles throughout the book, adapting themselves to different contexts as they arise. Our context in this chapter will begin with the famous “strength of weak ties” hypothesis from sociology [190], exploring outward from this point to more general settings as well. Let’s begin with some background and a motivating question. As part of his Ph.D. thesis research in the late 1960s, Mark Granovetter interviewed people who had recently changed employers to learn...

The KOJAK Group Finder: Connecting the Dots via Integrated Knowledge-Based and Statistical Reasoning

February 10, 2019July 6, 2017 by admin

Abstract

Link discovery is a new challenge in data mining whose primary concerns are to identify strong links and discover hidden relationships among entities and organizations based on low-level, incomplete and noisy evidence data. To address this challenge, we are developing a hybrid link discovery system called KOJAK that combines state-of-theart knowledge representation and reasoning (KR&R) technology with statistical clustering and analysis techniques from the area of data mining. In this paper we report on the architecture and technology of its first fully completed module called the KOJAK Group Finder. The Group Finder is capable of finding hidden groups and group members in large evidence databases. Our group finding approach addresses a variety of important LD challenges, such as being able to exploit heterogeneous and structurally rich evidence, handling the connectivity curse, noise and corruption as well as the capability to scale up to very large, realistic data sets. The first version of the KOJAK Group Finder has been successfully tested and evaluated on a...

Friendship Networks and Delinquency: The Relative Nature of Peer Delinquency

February 10, 2019July 6, 2017 by admin

Although acknowledging the importance of adolescent friendships in the etiology of delinquency, prior studies have yet to provide a detailed examination of the role of actual friendship networks in delinquency. Using data from the National Longitudinal Study of Adolescent Health (1995–1996), this study’s incorporation of friendship networks allows for a more rigorous conceptualization and measurement of peer delinquency based on carefully defined networks of adolescent friendships. Findings illustrate that friendship networks are very heterogenous in terms of members’ participation in delinquent behavior with the majority of adolescents belonging to networks containing both delinquent and non-delinquent friends. In support of differential association’s premise that delinquent behavior is influenced by the ratio of definitions favorable to those unfavorable to law violation (Sutherland, 1947), the proportion of delinquent friends in a respondent’s network is most strongly associated with respondents’ subsequent delinquency. This relative measure of peer delinquency is preferable to a measure of the absolute level of delinquency occurring by friends, the average delinquency committed by friends,...

Estimating the Effects of Friendship Networks on Health Behaviors of Adolescents

February 10, 2019July 6, 2017 by admin

Abstract

Researchers typically examine peer effects by defining the peer group broadly (all classmates, schoolmates, neighbors) because of the lack of friendship information in many data sources as well as to enable the use of plausibly exogenous variation in peer group composition across cohorts in the same school. This paper estimates the effects of friend’s health behaviors on own health behaviors for adolescents. A causal effect of friend’s health behaviors is identified by comparing similar individuals who have the same friendship opportunities because they attend the same school and make the same friendship choices, under the assumption that the friendship choice reveals information about an individual’s unobservables. We combine this identification strategy with a cross-cohort, within school design so that the model is identified based on across grade differences in the clustering of health behaviors within specific friendship options. This strategy allows us to separate the effect of friends behavior...

Mining For Offender Group Detection And Story Of A Police Operation

February 10, 2019July 6, 2017 by admin

Abstract

Since discovery of an underlying organisational structure from crime data leads the investigation to terrorist cells or organised crime groups, detecting covert networks are important to crime investigation. As shown in application of Offender Group Detection Model (OGDM), which is developed and tested on a theft network in Bursa, Turkey, use of effective data mining methods can reveal offender groups. OGDM detected seven ruling members of twenty network members. Based on initial findings of OGDM; thirty-four offenders are considered to be in a single offender group where seven of them were ruling members. After Operation Cash was launched, the police arrested the seven detected ruling members, and confirmed that the real crime network was consisting of 20 members of which 3 whom had never been previously identified or arrested. The police arrested 17 people, recovered worth U.S. $ 200,000 of stolen goods, and cash worth U.S. $ 180,000.

1 Introduction
Link analysis and group detection is a newly emerging research area which is

Contract Killings in Australia

February 10, 2019July 8, 2017 by admin

Introduction

The typical homicide in Australia is that of a male aged in his early to mid thirties who in an uncontrollable rage following some type of menial argument stabs another person, usually another male friend or acquaintance, to death. In such one-on-one interactions, the lethal outcome is rarely premeditated, but rather the spontaneous overreaction to a conflict situation. Moreover, there is the direct link between the victim and offender and most importantly, why the offender may have wanted to kill the victim. Important in understanding the context of homicide are the many reasons or motives associated with it, as well as the offender-victim relationship (Mouzos 2000; Polk 1994; Wolfgang 1958). Often a reason or motive can be derived from the offender-victim relationship. A proven motive, whilst not necessary to secure a conviction for murder, provides a sound basis for proving intent to kill and for contextualising the crime. Similar to the scenario described above, a traditional notion of homicide invokes the thought of ...

Read More!

Categories Contract Killing Leave a comment

Link Prediction in Affiliation Networks

February 10, 2019July 6, 2017 by admin

Applications of Link Prediction

1) Retail Recommender Systems
Used by e-commerce and marketing companies to recommend potential products to customers.
Amazon.com: recommends books, music and retail products to potential customers.
SkyMall.com: recommends retail products to potential customers.
Netflix.com and MovieLens.org: recommend movies to potential viewers.
[Riedl & Konstan 2002]

Applications of Link Prediction

2) Information Filtering
Used by news forums, academic publication repositories and web search engines.
GroupLens: recommends current event news articles to subscribers.
ReferallWeb: recommends authors and information to people based on a trusted chain of referrals from other users.
CiteSeer: recommends scientific publications to researchers.
[Resnick & Varian 1997]

Applications of Link Prediction

3) Biological Systems – Protein Interactions
Due to the fact that protein-protein interaction networks contain hub proteins and contain triads and tetrads at a higher frequency than random networks, it is possible to predict future protein interactions. “We show that we can leverage the information encoded in consensus interaction patterns to generate high relevance predictions for new

Association Analysis: Basic Concepts And Algorithms

February 10, 2019July 6, 2017 by admin

Many business enterprises accumulate large quantities of data from their day to day operations. For example, huge amounts of customer purchase data are collected daily at the checkout counters of grocery stores. Table 5.1 gives an example of such data, commonly known as market basket transactions. Each row in this table corresponds to a transaction, which contains a unique identifier labeled TID and a set of items bought by a given customer. Retailers are interested in analyzing the data to learn about the purchasing behavior of their customers. Such valuable information can be used to support a variety of business-related applications such as marketing promotions, inventory management, and customer relationship management. This chapter presents a methodology known as association analysis, which is useful for discovering interesting relationships hidden in large data sets. The uncovered relationships can be represented in the form of sets of items present in many transactions, which are known as frequent item sets, or association rules, that represent relationships between two item sets....

Clique Relaxation Models of Clusters in Networks

February 10, 2019July 6, 2017 by admin

Abstract

Clique relaxation models that were originally introduced in the literature on social network analysis are not only gaining increasing popularity in a wide spectrum of complex network applications, but also keep garnering attention of mathematicians, computer scientists, and operations researchers as a promising avenue for fruitful theoretical investigations. This chapter describes the origins of clique relaxation concepts and provides a brief overview of mathematical programming formulations for the corresponding optimization problems, algorithms proposed to solve these problems, and selected real-life applications of the models of interest.

1 Introduction

Social networks represent certain types of social interaction, such as acquaintance, friendship, or collaboration between people or groups of people that are referred to as actors. In social networks, vertices (nodes, dots) usually stand for actors, and edges (arcs, links, lines) represent the pairwise relations or interactions between the actors. As an illustration of a...

Dismantling Criminal Networks: Can Node Attributes Play A Role?

February 10, 2019July 8, 2017 by admin

Abstract

Internationally, there is recognition of the need to more clearly understand drug markets and the criminal syndicates that operate within them, in order to target drug law enforcement interventions in the most effective ways. The current project aims to fill some of the gaps in knowledge about the structure of drug trafficking networks using SNA, and to evaluate the impact of different types of law enforcement interventions directed at drug trafficking networks. We build on earlier work in which judges‘ sentencing comments were used to build a network map of a drug trafficking syndicate which operated in Australia in the 1990s. As well as producing a network map, this study was also able to identify the role that each individual played within the syndicate. We wish to explore the effectiveness of different hypothetical intervention strategies that aim to dismantle the network. First we investigate the structure of the network and show that it shares some properties of scale-free networks. Then four enforcement

Is It Who You Know, or How Many That Counts? Criminal Networks and Cost Avoidance in a Sample of Young Offenders

February 10, 2019July 8, 2017 by admin

Abstract

The aim of the current study is to assess whether criminal networks can help young offenders avoid contacts with the criminal justice system. We examine the association between criminal network and cost avoidance specifically for the crime of cannabis cultivation in a rural region in Quebec, Canada. A self‐report delinquency survey, administered to the region's quasi‐population of high‐school students (N = 1,166), revealed that a total of 175 adolescents had participated in the cannabis cultivation industry (a 15% lifetime prevalence rate). Forty‐seven respondents (27%), including 29 who were arrested, reported having participated in a cultivation site that was detected by the police. Results indicate that “who you know” matters in the cultivation industry, and is an important independent predictor of arrest: very few young growers who were embedded in adult networks were apprehended. Conversely, embeddedness in a youth network emerged as an independent risk factor, especially embeddedness in larger networks.

Additional Resource: Is It Who You Know, Or How Many That Counts? Criminal Networks And Cost Avoidance In A Sample Of Young Offenders (3187 downloads )

Older posts

Page1 Page2 Next →