Seeding influential nodes in non-submodular models of information diffusion

Anshelevich, Elliot; Hate, Ameya; Magdon‐Ismail, Malik

doi:10.1007/s10458-014-9254-4

Cited by 10 publications

(5 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Under non-submodular models, [11], [16] reuse a pure greedy-search-based algorithm, which is not specific to submodularity. To improve/quantify the algorithm performance, [13], [15] approximate the original problem by simpler submodular functions. In addition, Sandwich Approximation is employed in [13], [14] to bound the original problems by tractable submodular functions.…”

Section: A Further Discussion On Related Workmentioning

confidence: 99%

Graphical approach for influence maximization in social networks under generic threshold-based non-submodular model

Kaplan

2017

2017 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

As a widely observable social effect, influence diffusion refers to a process where innovations, trends, awareness, etc. spread across the network via the social impact among individuals. Motivated by such social effect, the concept of influence maximization is coined, where the goal is to select a bounded number of the most influential nodes (seed nodes) from a social network so that they can jointly trigger the maximal influence diffusion. A rich body of research in this area is performed under statistical diffusion models with provable submodularity, which essentially simplifies the problem as the optimal result can be approximated by the simple greedy search. When the diffusion models are non-submodular, however, the research community mostly focuses on how to bound/approximate them by tractable submodular functions so as to estimate the optimal result. In other words, there is still a lack of efficient methods that can directly resolve non-submodular influence maximization problems. In this regard, we fill the gap by proposing seed selection strategies using network graphical properties in a generalized threshold-based model, called influence barricade model, which is non-submodular. Specifically, under this model, we first establish theories to reveal graphical conditions that ensure the network generated by node removals has the same optimal seed set as that in the original network. We then exploit these theoretical conditions to develop efficient algorithms by strategically removing less-important nodes and selecting seeds only in the remaining network. To the best of our knowledge, this is the first graph-based approach that directly tackles non-submodular influence maximization. Evaluations on both synthetic and real-world Facebook/Twitter datasets confirm the superior efficiency of the proposed algorithms, which are orders of magnitude faster than benchmarks for large networks.

show abstract

Section: A Further Discussion On Related Workmentioning

confidence: 99%

Graphical approach for influence maximization in social networks under generic threshold-based non-submodular model

Kaplan

2017

2017 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

show abstract

“…Moreover, there are more existing approaches to understanding information diffusion patterns. The projected greedy approach for non-sub-modular problems [80] was recently proposed to populate the useful seeds in social networks. This approach can identify the partial optimisation for understanding the information diffusion.…”

Section: Information Diffusion Models and Methodsmentioning

confidence: 99%

Social big data: Recent achievements and new challenges

2016

View full text Add to dashboard Cite

a b s t r a c tBig data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms. (D. Camacho). petabytes (and even exabytes) in size, and the massive sizes of these datasets extend beyond the ability of average database software tools to capture, store, manage, and analyse them effectively.The concept of big data has been defined through the 3V model, which was defined in 2001 by Laney [5] as: "high-volume, highvelocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making". More recently, in 2012, Gartner [6] updated the definition as follows: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization". Both definitions refer to the three basic features of big data: Volume, Variety, and Velocity. Other organisations, and big data practitioners (e.g., researchers, engineers, and so on), have extended this 3V model to a 4V model by including a new "V": Value [7]. This model can be even extended to 5Vs if the concepts of Veracity is incorporated into the big data definition.Summarising, this set of * V-models provides a straightforward and widely accepted definition related to what is (and what is not) a big-data-based problem, application, software, or framework. These concepts can be briefly described as follows [5,7]:• Volume: refers to large amounts of any kind of data from any different sources, including mobile digital data creation devices and digital devices. The benefit from gathering, processing, and analysing these large amounts of data generates a number http://dx.

show abstract

“…For instance, empirical approaches focus on the influence of the interactions between individuals in online social networks at the micro level [12,13], mathematical models study the macro process of online information diffusion [14,15], and network analyses often investigate the effect of network structure on information diffusion [16][17][18]. The multi-agent simulation method, as a new modeling and analysis perspective in studying the evolution of group behavior [19], has been widely applied to investigate information diffusion [19][20][21]. These methods focus on microindividual interactions or macro-evolution of OSNID [22].…”

Section: Introductionmentioning

confidence: 99%

Online Social Network Information Dissemination Integrating Overconfidence and Evolutionary Game Theory

et al. 2021

View full text Add to dashboard Cite

Public opinion inversion and other nonlinear phenomena often occur during online social network information dissemination (OSNID) in modern society. To explore the influence of overconfidence on OSNID, we develop a multi-agent simulation model integrating overconfidence and evolutionary game s, as previous research has scarcely paid any attention to irrational behavior in information dissemination. This integrated paradigm provides an effective tool for managers to control the spread of OSNID and reduce the harm caused by rumors. The theoretical model is constructed from the perspective of an evolutionary game and , combined with overconfidence theory, three overconfidence scenarios are designed: benefit overconfidence, cost overconfidence, and both benefit and cost overconfidence. Then, a multi-agent simulation model of OSNID under different overconfidence scenarios is realized. The proposed simulation model exhibited better performance (e.g., faster diffusion speed) than the traditional Bass model; its performance was also validated by comparison with real-world cases. The results demonstrate that (1) with increasing benefit overconfidence, the convergence speed of OSNID will be accelerated, and the user group will reach stability at a faster speed. (2) With increasing cost overconfidence, the user's decision will change from adopting dominant to titfor-tat, and the adoption ratio will gradually decrease. (3) Compared with overconfidence in earnings, overconfidence in costs is more conducive to improving stability. This study attempts to provide new ideas for OSNID and attempts to propose an integration framework for behavioral theory and simulation methods.

show abstract

Seeding influential nodes in non-submodular models of information diffusion

Cited by 10 publications

References 45 publications

Graphical approach for influence maximization in social networks under generic threshold-based non-submodular model

Graphical approach for influence maximization in social networks under generic threshold-based non-submodular model

Social big data: Recent achievements and new challenges

Online Social Network Information Dissemination Integrating Overconfidence and Evolutionary Game Theory

Contact Info

Product

Resources

About