部分论文总结

1

标题：

     英文：Topic-aware Social Influence Propagation Models

     中文：话题感知社会影响力传播模型

来源：ICDM2012

摘要：

We study social influence from a topic modeling perspective. We introduce novel topic-aware influence-driven propagation models that experimentally result to be more accurate in describing real-world cascades than the standard propagation models studied in the literature. In particular, we first propose simple topic-aware extensions of the well-known Independent Cascade and Linear Threshold models. Next, we propose a different approach explicitly modeling authoritativeness, influence and relevance under a topic-aware perspective. We devise methods to learn the parameters of the models from a dataset of past propagations. Our experimentation confirms the high accuracy of the proposed models and learning schemes.

摘要翻译：

     我们从话题建模的方面来研究社会影响力。我们引入新的话题感知影响力驱动传播模型，我们的模型的实验结果在描述现实世界的信息流（cascade）的时候比文献中的标准传播模型更为精准。特别地，我们首先基于著名的Independent Cascade and Linear Threshold模型进行扩展，提出了一种简单的话题感知模型。之后，我们提出了一种不同的方法明确地对权威性、影响力、相关性等从话题感知的角度进行建模，我们发明了一种方法，从历史除传播数据集中对模型的参数进行学习。我们的实验确认了我们提出模型和学习方案的高精确性。

2

标题：

     英文：Clash of the Contagions: Cooperation and Competition in Information Diffusion

     中文：传染的碰撞：信息传播中的合作与竞争

来源：ICDM2012

摘要：

In networks, contagions such as information, purchasing behaviors, and diseases, spread and diffuse from node to node over the edges of the network. Moreover, in real-world scenarios multiple contagions spread through the network simultaneously. These contagions not only propagate at the same time but they also interact and compete with each other as they spread over the network.

While traditional empirical studies and models of diffusion consider individual contagions as independent and thus spreading in isolation, we study how different contagions interact with each other as they spread through the network. We develop a statistical model that allows for competition as well as cooperation of different contagions in information diffusion. Competing contagions decrease each other’s probability of spreading, while cooperating contagions help each other in being adopted throughout the network.

We evaluate our model on 18,000 contagions simultaneously spreading through the Twitter network. Our model how different contagions interact with each other and then uses these interactions to more accurately predict the diffusion of a contagion through the network. Moreover, the model also provides a compelling hypothesis for the principles that govern content interaction in information diffusion. Most importantly, we find very strong effects of interactions between contagions. Interactions cause a relative change in the spreading probability of a contagion by 71% on the average.

摘要翻译：

在网络中，诸如信息、购买行为、疾病之类的传染（contagions ），在节点与节点之间通过网络的边进行扩散和传播。此外，在现实世界的场景中多种传染通过网络同时地传播。这些传染不仅在同一时间传播，而且当它们在网络上扩散的时候，它们之间还会进行互动与竞争。

传统的实验研究与传播模型将单独的传染视为独立的，对其扩散过程也视为孤立的，而我们则研究了不同传染在网上扩散的时候它们相互之间如何进行互动。我们构建了一个统计模型，该模型将信息传播中不同传染的竞争与合作纳入考虑。竞争的传染减少了它们各自扩散的可能性，而传染的合作则促进它们各自在网络上的传播。

我们基于18000个传染在Twitter上同时传播情况评估了我们的模型。我们的模型能够学习不同的传染之间如何互动，并能够利用这些互动对网络上的传染进行更精准地预测。此外，模型还能够提供令人信服的假设来描述控制信息传播中内容互动的原理。更为重要的是，我们找到了传染之间的互动所造成的强烈影响。互动导致了传染的扩散概率产生了相对的改变，其数值平均为71%。

3

标题：

     英文：Diffusion of Information in Social Networks: Is It All Local?

     中文：社交网络中的信息传播：它是完全局部性的吗？

来源：ICDM2012

摘要：

Recent studies on the diffusion of information in social networks have largely focused on models based on the influence of local friends. In this paper, we challenge the generalizability of this approach and revive theories introduced by social scientists in the context of diffusion of innovations to model user behavior. To this end, we study various diffusion models in two different online social networks; Digg and Twitter. We first evaluate the applicability of two representative local influence models and show that the behavior of most social networks users are not captured by these local models. Next, driven by theories introduced in the diffusion of innovations research, we introduce a novel diffusion model called Gaussian Logit Curve Model (GLCM) that models user behavior with respect to the behavior of the general population. Our analysis shows that GLCM captures user behavior significantly better than local models, especially in the context of Digg. Aiming to capture both the local and global signals, we introduce various hybrid models and evaluate them through statistical methods. Our methodology models each user separately, automatically determining which users are driven by their local relations and which users are better defined through adopter categories, therefore capturing the complexity of human behavior.

摘要翻译：

最近，关于社交网络中的信息传播的研究主要关注于一类模型，这些模型基于局部好友的影响力。在这篇论文中，我们挑战这种方法的普遍性，并且重用了社会学家提出的创新扩散理论来对用户行为建模。为此，我们在Digg和Twitter这两个不同的社交网络上研究了多种模型。我们首先评估了两种代表性的局部影响力模型，结果表明大部分社交网络的用户的行为未能被这些局部模型所捕获。接着，我们被创新扩散研究中提出的理论所驱动，提出了一种新的传播模型，我们称之为Gaussian Logit Curve Model (GLCM) ，该模型对用户行为根据一般人群的行为进行建模。我们的分析表明GLCM在捕获用户行为方面要明显优于局部模型，特别是在Digg中。我们的目标在于捕获局部和全局的信号，我们提出了一种混合模型，并利用统计方法对它们进行评估。我们的方法对用户分别建模，自动地测定哪些用户被局部关系所驱动，以及哪些用户更好地通过采购者的类型（ adopter categories）来定义，因此能够捕获人类行为的复杂性。