Active learning for networked data based on non-progressive diffusion model

Yang, Zhilin; Tang, Jie; Xu, Bin; Xing, Chunxiao

doi:10.1145/2556195.2556223

Cited by 5 publications

(5 citation statements)

References 32 publications

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“…[36] propose a sample-based algorithm with ∈ (0, 1] to maximize the influence. Another sampling diffusion model are proposed by Yang, Tang, et al [251]. They develop an active learning technique to alleviate the problem for how to collect sufficient labeled samples for training an accurate classification.…”

Section: Heterogeneous Modelmentioning

confidence: 99%

Influence analysis: A survey of the state-of-the-art

Han¹,

Li²

2018

Mathematical Foundations of Computing

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Online social networks have seen an exponential growth in number of users and activities recently. The rapid proliferation of online social networks provides rich data and infinite possibilities for us to analyze and understand the complex inherent mechanism which governs the evolution of the new online world. This paper summarizes the state-of-art research results on social influence analysis in a broad sense. First, we review the development process of influence analysis in social networks based on several basic conceptions and features in a social aspect. Then the online social networks are discussed. After describing the classical models which simulate the influence spreading progress, we give a bird's eye view of the up-to-date literatures on influence diffusion models and influence maximization approaches. Third, we present the applications including web services, marketing, and advertisement services which based on the influence analysis. At last, we point out the research challenges and opportunities in this area for both industry and academia reference. 201 202 MENG HAN AND YINGSHU LIachieved. Even so, prior to the Internet, quantitative data of social networks were scanty and the further influence analysis in social networks was in the slow-lane. In 2007, Nicholas et al. [47] published their years of research results based on the historical data from the spreading of obesity over 32 years. From the same research filed, David et al. [11] proposed another idea about the spread of obesity in social networks based on the simulations which further considered the group effect in obesity spreading. Both works tried to explore how the influence diffusion in social networks affects obesity. In their model, individuals' influence over each other rely on food intake and physical activities [70]. Since other models consider obesity as a "contagious" phenomenon that can be caught if most social contracts are deemed obese, the interaction of social networks with environmental factors could not be explored. It was not accounted for in the general model where the social networks were proposed as a means to mitigate the obesity epidemic. Many other research results have been obtained recently, such as smoking behavior [48][52], happiness [68], and loneliness [25][117] which spread along a social network over time.At an American high school, Salath et al.[207] obtained high-resolution data of close proximity interactions during a typical day, and their work helps with the reconstruction of a social network for infectious disease transmission by using wireless sensor network technology [246,98]. Through simulations, they showed that targeted immunization using the contact-network data is much more effective than random immunization. Stehle et al. [216] report a similar result like Salath [207] in a French primary school. The team headed by Stehle also provided several public-health implications of infectious diseases by collecting a period of history data from their experiments. By analyzing the real experiments in two ...

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Section: Heterogeneous Modelmentioning

confidence: 99%

Influence analysis: A survey of the state-of-the-art

Han¹,

Li²

2018

Mathematical Foundations of Computing

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“…当考虑用户状态的变化情况时, 倘若用户只能一次性地从未激活态切换到激活态, 即状态切换不可逆, 则为递进式 (progressive) 模型, 例如独立级联模型与线性阈值模型都属于递进式模型. 反之如果状态之间可以随意切换, 则为非递进式 (non-progressive) 模型 [17,18] . 递进式模型一般描述信息与商品等的传播, 而非递进式模型一般刻画观点与情绪等的传播.…”

Section: 在独立级联模型中网络中每条关系unclassified

Survey of social influence analysis and modeling

Zhang

Tang

2017

Sci. Sin.-Inf.

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Social influence occurs when one's opinions or behaviors are affected by others. It forms a prevalent, complex, and subtle force that governs the dynamics of social networks. With the rapid proliferation of online social networks such as Twitter, Facebook, Yelp, and Amazon, modeling the influence diffusion mechanism and quantitatively measuring social influence between people become more and more critical for algorithms behind features such as friend recommendations, expert finding, and behavior prediction. In addition, they can also benefit the development of virtual marketing and supervision by public opinions. Social influence has been extensively studied and has recently been attracting great attention from different communities. This paper introduces the study and the future work of social influence, in particular, influence tests, modeling, and measurement.

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“…), but where acquiring the associated labels of interest for these nodes is much more expensive. As a result, some prior studies have studied active learning for relational data [8], [20], [9], [11], [10], [21]. We discuss the most relevant such work below.…”

Section: Active Learning With Lbcmentioning

confidence: 99%

Relational active learning for link-based classification

McDowell¹

2015

2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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Many information tasks involve objects that are explicitly or implicitly connected in a network (or graph), such as webpages connected by hyperlinks or people linked by "friendships" in a social network. Research on link-based classification (LBC) has shown how to leverage these connections to improve classification accuracy. Unfortunately, acquiring a sufficient number of labeled examples to enable accurate learning for LBC can often be expensive or impractical. In response, some recent work has proposed the use of active learning, where the LBC method can intelligently select a limited set of additional labels to acquire, so as to reduce the overall cost of learning a model with sufficient accuracy. This work, however, has produced conflicting results and has not considered recent progress for LBC inference and semi-supervised learning. In this paper, we evaluate multiple prior methods for active learning and demonstrate that none consistently improve upon random guessing. We then introduce two new methods that both seek to improve active learning by leveraging the link structure to identify nodes to acquire that are more representative of the underlying data. We show that both approaches have some merit, but that one method, by proactively acquiring nodes so as to produce a more representative distribution of known labels, often leads to significant accuracy increases with minimal computational cost.

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Active learning for networked data based on non-progressive diffusion model

Cited by 5 publications

References 32 publications

Influence analysis: A survey of the state-of-the-art

Influence analysis: A survey of the state-of-the-art

Survey of social influence analysis and modeling

Relational active learning for link-based classification

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