Reducing explanations of victimization to a person’s risky lifestyle has stalled growth in theories of victimization. Drawing from Carlo Morselli’s contributions to social network analysis, the current study extended past research on community-based co-offending networks and victimization in two ways. First, the current study more comprehensively measured a person’s criminogenic network by also examining the contribution of conflict ties and social ties to victimization. Second, we investigated whether serious victimization was prospectively associated with social network characteristics. Data were used on 99 participants from the Incarcerated Serious Violent Young Offender Study who had criminogenic connections within the city of Surrey, BC. Time-dependent covariate survival analysis was used to model the relationship between network characteristics and time to victimization. Time-series ordinary least squares regression was used to examine whether serious victimization predicted network characteristics. Participants with a greater number of co-offending ties experienced serious victimization significantly later. As evidence of the reciprocal nature of the victimization–network relationship, victimization predicted a greater number of future criminogenic connections in the co-offending tie, social tie, and prison tie networks. Findings have implications for network-based intervention models.

中文翻译：

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探索严重受害与犯罪网络之间的互惠关系

将受害的解释减少到一个人的危险生活方式已经阻碍了受害理论的发展。借鉴 Carlo Morselli 对社会网络分析的贡献，本研究以两种方式扩展了过去对基于社区的共同犯罪网络和受害网络的研究。首先，当前的研究通过检验冲突关系和社会关系对受害的贡献，更全面地衡量了一个人的犯罪网络。其次，我们调查了严重受害是否与社交网络特征前瞻性相关。数据用于在不列颠哥伦比亚省萨里市有犯罪联系的被监禁严重暴力青年罪犯研究中的 99 名参与者。时间依赖性协变量生存分析用于模拟网络特征与受害时间之间的关系。时间序列普通最小二乘回归用于检查严重受害是否预测网络特征。具有更多共同犯罪关系的参与者在很晚的时候经历了严重的受害。作为受害网络关系互惠性质的证据，受害预测未来在共同犯罪关系、社会关系和监狱关系网络中会有更多的犯罪联系。研究结果对基于网络的干预模型有影响。具有更多共同犯罪关系的参与者在很晚的时候经历了严重的受害。作为受害网络关系互惠性质的证据，受害预测未来在共同犯罪关系、社会关系和监狱关系网络中会有更多的犯罪联系。研究结果对基于网络的干预模型有影响。具有更多共同犯罪关系的参与者在很晚的时候经历了严重的受害。作为受害网络关系互惠性质的证据，受害预测未来在共同犯罪关系、社会关系和监狱关系网络中会有更多的犯罪联系。研究结果对基于网络的干预模型有影响。