Real world networks comprise of number of communities that are weakly linked to each other. Discovering such communities is an important task in social networks. Social networks also consist of overlapping communities in which some nodes are common to multiple communities. While existing approaches give promising results on detecting the overlapping communities, they neglect the importance of overlap nodes in the communities. Overlap nodes are the nodes which act as interface between multiple communities. In this paper, we first find the overlapping communities and then find the influence and importance of overlap nodes in the communities to which it belongs.

With the advent of web 2.0 and anonymous free Internet services available to almost everyone, social media has gained immense popularity in disseminating information. It has become an effective channel for advertising and viral marketing. People rely on social networks for news, communication and it has become an integral part of our daily lives. But due to the limited accountability of users, it is often misused for the spread of rumors. Such rumor diffusion hampers the credibility of social media and may spread social panic. Analyzing rumors in social media has gained immense attention from the researchers in the past decade. In this paper the authors provide a survey of work in rumor analysis, which will serve as a stepping-stone for new researchers. They organized the study of rumors into four categories and discussed state of the art papers in each with an in-depth analysis of results of different models used and a comparative analysis between approaches used by different authors.

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With the advent of web 2.0 and anonymous free Internet services available to almost everyone, social media has gained immense popularity in disseminating information. It has become an effective channel for advertising and viral marketing. People rely on social networks for news, communication and it has become an integral part of our daily lives. But due to the limited accountability of users, it is often misused for the spread of rumors. Such rumor diffusion hampers the credibility of social media and may spread social panic. Analyzing rumors in social media has gained immense attention from the researchers in the past decade. In this paper the authors provide a survey of work in rumor analysis, which will serve as a stepping-stone for new researchers. They organized the study of rumors into four categories and discussed state of the art papers in each with an in-depth analysis of results of different models used and a comparative analysis between approaches used by different authors.

This paper proposes a steganography technique to hide secret messages in the least significant bits of the pixels of the cover image which are most different from their neighbourhood pixels. Since such pixels are the areas in an image which correspond to great changes in the visual property, they make a better option to hold secret data than smoother regions of the image, where a small distortion is much more noticeable. This paper uses LSB Matching algorithm for data embedding and the embedding method is adaptive to the amount of data in the message. Smaller is the amount of data, higher the value of threshold is chosen. The proposed technique is tested on BOWS2 Database consisting 10000 images and is compared with current steganography techniques for their robustness against various steganalysis methods. The proposed technique has given overall better results as compared to other techniques.