Peer and Authority Pressure in Information-Propagation Models

Anagnostopoulos, Aris; Brova, George; Terzi, Evimaria

doi:10.1007/978-3-642-23780-5_15

Cited by 18 publications

(16 citation statements)

References 14 publications

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“…We can observe that the PA model fails to qualitatively reproduce the fragility evolution pattern (r k initially increases and after a time point it starts decreasing gradually leading to robust enough graphs). More precisely, the first observation is that for small values of m (i.e., m = [2,4]), the robustness seems to remain almost constant, independently of the graph size. This can be explained by the structural properties of the graphs generated by the PA model.…”

Section: Preferential Attachment Modelmentioning

confidence: 94%

“…To provide a more thorough examination of this behavior, we study the robustness index of PA graphs at different scales and for several values of parameter m. Figure 9 depicts the r k value for PA graphs of various sizes (5-350 K nodes) and for a wide range of values for parameter m (m = [2,4,6,8]). We can observe that the PA model fails to qualitatively reproduce the fragility evolution pattern (r k initially increases and after a time point it starts decreasing gradually leading to robust enough graphs).…”

Section: Preferential Attachment Modelmentioning

confidence: 99%

“…Later, we will see how to utilize the observed robustness properties of real graphs in order to detect temporal anomalies in social graphs. Other problem domains are searching in networks [30], graph compression and summarization [24,33,48], graph clustering [46] and information-influence propagation in social networks [4,34].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Estimating robustness in large social graphs

2014

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Given a large social graph, what can we say about its robustness? Broadly speaking, the property of robustness is crucial in real graphs, since it is related to the structural behavior of graphs to retain their connectivity properties after losing a portion of their edges/nodes. Can we estimate a robustness index for a graph quickly? Additionally, if the graph evolves over time, how this property changes? In this work, we are trying to answer the above questions studying the expansion properties of large social graphs. First, we present a measure that characterizes the robustness properties of a graph and also serves as global measure of the community structure (or lack thereof). We show how to compute this measure efficiently by exploiting the special spectral properties of real-world networks. We apply our method on several diverse real networks with millions of nodes, and we observe interesting properties for both static and time-evolving social graphs. As an application example, we show how to spot outliers and anomalies in graphs over time. Finally, we examine how graph generating models that mimic several properties of real-world graphs and behave in terms of robustness dynamics.

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Section: Preferential Attachment Modelmentioning

confidence: 94%

Section: Preferential Attachment Modelmentioning

confidence: 99%

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Estimating robustness in large social graphs

2014

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“…Zhang et al [17] have identified strong users using their friends' comments. Anagnostopoulos et al [18] have also identified users with high influence among information broadcast by users through social networks.…”

Section: Approaches Based On Users' Behaviormentioning

confidence: 99%

A Genetic Programming based Algorithm for Predicting Exchanges in Electronic Trade using Social Networks’ Data

Poor¹,

Shiri²

2017

ijacsa

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Abstract-Purpose of this paper is to use Facebook dataset for predicting Exchanges in Electronic business. For this purpose, first a dataset is collected from Facebook users and this dataset is divided into two training and test datasets. First, an advertisement post is sent for training data users and feedback from each user is recorded. Then, a learning machine is designed and trained based on these feedbacks and users' profiles. In order to design this learning machine, genetic programming is used. Next, test dataset is used to test the learning machine. The efficiency of the proposed method is evaluated in terms of Precision, Accuracy, Recall and F-Measure. Experiment results showed that the proposed method outperforms basic algorithm (based on J48) and random selection method in selecting objective users for sending advertisements. The proposed method has obtained Accuracy=74% and 73% earning ration in classifying users.

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“…Work on this topic encompasses the shrinking diameter and densification [25]; the power law for the mail response times of Einstein and Darwin, [30]; analysis of blog dynamics [16,26], and discovery of core-periphery patterns in blogs and news articles [15]; viral marketing [23,21]; meme tracking [24]; reciprocity analysis [14,6]; analysis of the role of weak and strong ties in information diffusion in mobile networks [31]; identification of important influencers [36]; prediction of service adoption in mobile communication networks [37]; information or cascade diffusion in social networks [9,4,8,38]; linguistic change in online forums, and predicting the user's lifespan based on her linguistic patterns [11]; peer and authority pressure in information propagation [7].…”

Section: Related Workmentioning

confidence: 99%

Influence Propagation: Patterns, Model and a Case Study

Lin

Raza

Lee

et al. 2014

Advances in Knowledge Discovery and Data Mining

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Abstract. When a free, catchy application shows up, how quickly will people notify their friends about it? Will the enthusiasm drop exponentially with time, or oscillate? What other patterns emerge? Here we answer these questions using data from the Polly telephone-based application, a large influence network of 72,000 people, with about 173,000 interactions, spanning 500MB of log data and 200 GB of audio data. We report surprising patterns, the most striking of which are: (a) the FIZZLE pattern, i.e., excitement about Polly shows a power-law decay over time with exponent of -1.2; (b) the RENDEZVOUS pattern, that obeys a power law (we explain RENDEZVOUS in the text); (c) the DISPERSION pattern, we find that the more a person uses Polly, the fewer friends he will use it with, but in a reciprocal fashion. Finally, we also propose a generator of influence networks, which generate networks that mimic our discovered patterns.

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Peer and Authority Pressure in Information-Propagation Models

Cited by 18 publications

References 14 publications

Estimating robustness in large social graphs

Estimating robustness in large social graphs

A Genetic Programming based Algorithm for Predicting Exchanges in Electronic Trade using Social Networks’ Data

Influence Propagation: Patterns, Model and a Case Study

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