As time evolves, communities in a social network may undergo various changes known as critical events. For instance, a community can either split into several other communities, expand into a larger community, shrink to a smaller community, remain stable or merge into another community. Prediction of critical events has attracted increasing attention in the recent literature. Learning the evolution of communities over time is a key step towards predicting the critical events the communities may undergo. This is an important and difficult issue in the study of social networks. In the work to date, there is a lack of formal approaches for modeling and predicting critical events over time. This motivates our effort to design a new statistical method for event prediction in order to make better use of histories of past changes. To this end, this paper proposes a sliding window analysis from which we develop a model that simultaneously exploits an autoregressive model and survival analysis techniques. The autoregressive model is employed here to simulate the evolution of the community structure, whereas the survival analysis techniques allow the prediction of future changes the community may undergo (dotnet ieee projects in Chennai).



•To predict critical events that a community may undergo, existing approaches adopt a two-step methodology.

•The first step involves modeling and tracking the changes: a dynamic network is modeled as a series of frozen networks, each representing the instance of the network at a given time point. 

•Then, a community detection algorithm is applied on each frozen network to identify the communities at the corresponding timestamp, ieee 2019 dot net projects chennai.

• Finally, a pair wise comparison is performed to identify critical events and track the communities over time. 

•The second step usually involves using a supervised learning approach, based on the features extracted from the identified communities, to predict the most probable next event an evolving community may undergo.



•Change in community structure was found but could not find approximately.

•Also was not able to predict the changes before occurrence.



•We have used two time-dependent models to define critical events evolving communities may undergo as temporal probability equations(dotnet mini projects in chennai). 

•In one of the models, based on survival analysis, we made use of the Cox regression function. In contrast to the conventional Cox model in which the input features and parameters are time independent, we have considered this information to evolve with time.

•In order to determine the feature values over time, we have used the VAR model to represent each observed feature as a combination of lag observations. Moreover, as suggested in survival analysis theory, we define the hazard function using probability density functions. 

•We propose a principled approach in which we first define the optimal window size. Once the window is defined, it is then used to identify and track communities over time and detect critical events they may undergo. 

•Then, we model the critical events as temporal equations in order to predict future critical events based on auto regression and survival analysis theories(dotnet diploma project centers chennai). 



Our approach utilizes topological features extracted from the evolving communities



•Determining window size



E. G. Tajeuna, M. Bouguessa, and S.Wang, “Tracking the evolution of community structures in time-evolving social networks,” in Proceedings of the International Conference on Data Science and Advanced  Analytics (DSAA), 2015, pp. 1–10. 


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