Tracing temporal communities and event prediction in dynamic social networks

Khafaei, Taleb; Taraghi, Alireza Tavakoli; Hosseinzadeh, Mehdi; Rezaee, Ali

doi:10.1007/s13278-019-0604-8

Cited by 8 publications

(7 citation statements)

References 30 publications

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“…Several classification algorithms have been introduced for model learning, each of which can have different accuracy in different situations. In the study performed on the EPDSN model (Khafaei et al, 2019), the SGD classification algorithm has shown higher accuracy. This is the reason for the use of this algorithm in this study.…”

Section: Epdsn Methodsmentioning

confidence: 99%

“…This paper used the features introduced in the EPDSN model based on Table 2, and aimed at analyzing the structure of communities across different time intervals. There are two types of these features: (1) qualitative features, which accord with the degree of centrality (Bonacich, 1972), betweenness, closeness, and eigenvector (Freeman, 1978), and (2) quantitive features, which are considered as the number of leader nodes of each community introduced in (Khafaei et al, 2019).…”

Section: Epdsn Methodsmentioning

confidence: 99%

“…The related event may transpire in an evolving community with the use of change rates of features. In the another research, authors introduced the Event Predicting in Dynamic Social Network (EPDSN) (Khafaei et al, 2019) method aimed at tracing temporal communities using a new definition of events for Dynamic OSNs. In this method, they used consecutive time intervals of social network to learn the predictive model used seven feature as community’s structural features from time interval t [i], and then detect the communities’ events occurred during transition from time interval t [i] to time interval t [i+1] as input.…”

Section: Related Workmentioning

confidence: 99%

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The Sensitivity of Community Extra-Structural Features on Event Prediction in Dynamic Social Networks

Khafaei

Taraghi

Hosseinzadeh

et al. 2022

Social Science Computer Review

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A dynamic Online Social Network is a special type of evolving complex network in which changes occur over time. The structure of a community may change over time due to the relationship changes between its members or with other communities. This is known as a community event. In this paper, we discussed the effect of important individual community features and the lengths of adequate time intervals considered in the analysis of the behavior of social networks on the prediction accuracy of each event. Furthermore, we introduced the extra-structural features as global social network features to justify the relationship between the lengths of time intervals used in the model training by using the best prediction accuracy of events. We found a relationship between the scale of network dynamics and the length of time intervals for observing the spread and decomposed events. Finally, by comparing the accuracy of the model based on time interval length which investigated based on cps-value in this study and using the Event Prediction in Dynamic Social Network (EPDSN) model, the hypothesis of a reverse relationship between cps growth rate and time interval length to obtain better prediction accuracy for both the spread and decomposed events.

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Section: Epdsn Methodsmentioning

confidence: 99%

Section: Epdsn Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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The Sensitivity of Community Extra-Structural Features on Event Prediction in Dynamic Social Networks

Khafaei

Taraghi

Hosseinzadeh

et al. 2022

Social Science Computer Review

Self Cite

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“…However, another group of researchers considered social networks dynamic, mapped in a sequence consisting of intervals of static networks in such a way that communities are extracted from a period independent from the periods before and after. This type of analysis allows us to evaluate the changes in the behaviour of communities by comparing their structures in two consecutive periods [14,23,26].…”

Section: Introductionmentioning

confidence: 99%

PopNode-PopCom: Identifying The Popular Nodes to Predict the Popular Communities in Dynamic Social Networks

Khafaei

Taraghi

Patil

2022

Preprint

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Social networks play an important role in human life, and the study of communities within them is of particular importance. In this research, sustainable communities called popular communities have been introduced and studied. In this regard, the life of social networks was considered in time snapshots. Then, the communities that which at least 45% of their members were present the next time snapshot were considered popular communities. In the next step, the structure of popular communities and their distinguishing features from other communities as well as some of their structural features along with the other centrality features were introduced. Finally, using the theory of rough set, the importance of the structural features was examined to predict popular communities using the rules derived from this theory. Also, the importance of popular nodes introduced in this study in terms of, Integrated Value of Influence (IVI), Collective Influence (CF) and Spreading Score was compared with the maximum nodes of these values in each of the popular communities. In this study, we tested the performance of our proposed model on the actual Facebook database. We used rough set theory to generate the rules and model learning. Leader-Node, Popular-Node, Closeness, Eigen Centralization, Betweenness and Community Density were used as features and Popular Community was used as a label. In the experiment, 10-Fold Cross-Validation was used for model learning and Standard Voting was used as a classifier in the ROSETTA toolkit environment. The experiment has shown that if the average of an entire community is low on closeness, betweenness, and eigenvector centrality but with a high number of popular nodes, the chances of the community remaining popular increase. In addition, the high density of the community is not a good criterion for the community to remain popular at all. It was also shown that with the help of these features, the popular communities were predicted with almost 84% accuracy. On the other hand, the nodes with the maximum values of IVI, CF and Spreading Score were a subset of popular nodes. This shows the importance of popular nodes in social networks which introduced in this study.

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“…Many traditional classification techniques (decision trees, SVM, etc.) have been used in previous work [8], [5], [9]. This literature often includes experiments that evaluate the contribution of engineered features [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Group-Node Attention for Community Evolution Prediction

Revelle¹,

Domeniconi²,

Gelman³

2021

Preprint

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Communities in social networks evolve over time as people enter and leave the network and their activity behaviors shift. The task of predicting structural changes in communities over time is known as community evolution prediction. Existing work in this area has focused on the development of frameworks for defining events while using traditional classification methods to perform the actual prediction. We present a novel graph neural network for predicting community evolution events from structural and temporal information. The model (GNAN) includes a group-node attention component which enables support for variable-sized inputs and learned representation of groups based on member and neighbor node features. A comparative evaluation with standard baseline methods is performed and we demonstrate that our model outperforms the baselines. Additionally, we show the effects of network trends on model performance.

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Tracing temporal communities and event prediction in dynamic social networks

Cited by 8 publications

References 30 publications

The Sensitivity of Community Extra-Structural Features on Event Prediction in Dynamic Social Networks

The Sensitivity of Community Extra-Structural Features on Event Prediction in Dynamic Social Networks

PopNode-PopCom: Identifying The Popular Nodes to Predict the Popular Communities in Dynamic Social Networks

Group-Node Attention for Community Evolution Prediction

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