Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining最新文献

Social networks play a fundamental role in the diffusion of innovation through peers’ influence on adoption. Thus, network position including a wide range of network centrality measures has been used to describe individuals’ affinity to adopt an innovation and their ability to propagate diffusion. Yet, social networks are assortative in terms of susceptibility and influence and in terms of network centralities as well. This makes the identification of influencers difficult especially since susceptibility and centrality do not always go hand in hand. Here, we propose the Top Candidate algorithm, an expert recommendation method, to rank individuals based on their perceived expertise, which resonates well with the assortative mixing of innovators and early adopters in networks. Leveraging adoption data from two online social networks that are assortative in terms of adoption but represent different levels of assortativity of network centralities, we demonstrate that the Top Candidate ranking is more efficient in capturing innovators and early adopters than other widely used indices. Top Candidate nodes adopt earlier and have higher reach among innovators, early adopters and early majority than nodes highlighted by other methods. These results suggest that the Top Candidate method can identify good seeds for influence maximization campaigns on social networks.

社交网络通过同伴对采用的影响，在创新扩散中发挥了重要作用。因此，包括广泛的网络中心性度量在内的网络位置被用来描述个体采用创新的亲和力及其传播扩散的能力。然而，社交网络在易感性和影响力以及网络中心性方面都是分类的。这使得确定影响者变得困难，特别是因为易感性和中心性并不总是齐头并进。在这里，我们提出了Top Candidate算法，一种专家推荐方法，根据他们感知到的专业知识对个人进行排名，这与网络中创新者和早期采用者的分类混合产生了很好的共鸣。利用来自两个在线社交网络的采用数据，这两个网络在采用方面是分类的，但代表了网络中心性的不同分类水平，我们证明了Top Candidate排名在捕获创新者和早期采用者方面比其他广泛使用的指数更有效。与其他方法突出的节点相比，顶级候选节点采用得更早，在创新者、早期采用者和早期大众中具有更高的影响力。这些结果表明，Top Candidate方法可以识别出社交网络上影响力最大化活动的良好种子。

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引用次数: 0

Survival analysis for user disengagement prediction: question-and-answering communities' case 用户脱离预测的生存分析:问答社区案例

Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining

Pub Date : 2022-03-29 DOI: 10.48550/arXiv.2203.15255

H. A. Firouzjaei

We used survival analysis to model user disengagement in three distinct questions-and-answering communities in this work. We used the complete historical data of {Politics, Data Science, Computer Science} Stack Exchange communities from their inception until May 2021, which include the information about all users who were members of one of these three communities. Furthermore, formulating the user disengagement prediction as a survival analysis task, we utilised two survival analysis techniques to model and predict the probabilities of members of each community becoming disengaged. Our main finding is that the likelihood of users with even a few contributions staying active is noticeably higher than the users who were making no contributions; this distinction may widen as time passes. Moreover, the results of our experiments indicate that users with more favourable views towards the content shared on the platform may stay engaged longer. Finally, the observed pattern holds for all three communities, regardless of their themes.

在这项工作中，我们使用生存分析来模拟三个不同的问答社区的用户脱离。我们使用了{政治，数据科学，计算机科学}Stack Exchange社区从成立到2021年5月的完整历史数据，其中包括这三个社区之一的所有用户的信息。此外，将用户脱离预测作为生存分析任务，我们利用两种生存分析技术来建模和预测每个社区成员脱离的概率。我们的主要发现是，即使有少量贡献的用户保持活跃的可能性也明显高于没有贡献的用户;随着时间的推移，这种区别可能会扩大。此外，我们的实验结果表明，对平台上分享的内容持更有利观点的用户可能会保持更长的时间。最后，观察到的模式适用于所有三个社区，而不管它们的主题是什么。

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引用次数: 1

Mapping the Complexity of Suicide by Combining Participatory Modeling and Network Science. 参与式模型与网络科学相结合，映射自杀的复杂性。

Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining

Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488271

Philippe J Giabbanelli, Michael C Galgoczy, Duc M Nguyen, Romain Foy, Ketra L Rice, Nisha Nataraj, Margaret M Brown, Christopher R Harper

Suicide rates are steadily increasing among youth in the USA. Although several theories and frameworks of suicide have been developed, they do not account for some of the features that define suicide as a complex problem, such as a large number of interrelationships and cycles. In this paper, we create the first c omprehensive m ap o f a dverse c hildhood experiences (ACEs) and suicide for youth, by combining a participatory approach (involving 15 subject-matter experts) and network science. This results in a map of 946 edges and 361 concepts, in which we identify ACEs to be the most important factor (per degree centrality). The map is openly shared with the community to support further network analyses (e.g., decomposition into clusters). Similarly to the high-impact Foresight Map developed in the context of obesity, the largest map on suicide and ACEs to date presented in this paper can start a discussion at the crossroad of suicide research and network science, thus bringing new means to address a complex public health challenge.

美国年轻人的自杀率正在稳步上升。尽管已经发展了一些关于自杀的理论和框架，但它们并没有考虑到将自杀定义为一个复杂问题的一些特征，例如大量的相互关系和循环。在本文中，我们通过将参与式方法(涉及15名主题专家)和网络科学相结合，创建了第一个关于不同童年经历(ace)和青少年自杀的综合地图。这导致了946条边和361个概念的地图，其中我们确定ace是最重要的因素(每度中心性)。该地图与社区公开共享，以支持进一步的网络分析(例如，分解成集群)。与在肥胖背景下开发的高影响力的Foresight Map类似，本文中迄今为止最大的自杀和ace地图可以在自杀研究和网络科学的十字路口开始讨论，从而为解决复杂的公共卫生挑战带来新的手段。

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引用次数: 9

Optimal control of a delayed rumor propagation model with saturated control functions and L1-type objectives 具有饱和控制函数和l1型目标的延迟谣言传播模型的最优控制

Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining

Pub Date : 2020-08-26 DOI: 10.1007/s13278-020-00685-0

A. Abta, H. Laarabi, M. Rachik, H. Alaoui, Salahaddine Boutayeb

引用次数: 4

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