Mitul Tiwari

Global Diffusion via Cascading Invitations: Structure, Growth, and Homophily

Many of the worlds most popular websites catalyze their growth through invitations from existing members. New members can then in turn issue invitations, and so on, creating cascades of member signups that can spread on a global scale. Although these diffusive invitation processes are critical to the popularity and growth of many websites, they have rarely been studied, and their properties remain elusive. For instance, it is not known how viral these cascades structures are, how cascades grow over time, or how diffusive growth affects the resulting distribution of member characteristics present on the site. In this paper, we study the diffusion of LinkedIn, an online professional network comprising over 332 million members, a large fraction of whom joined the site as part of a signup cascade. First we analyze the structural patterns of these signup cascades, and find them to be qualitatively different from previously studied information diffusion cascades. We also examine how signup cascades grow over time, and observe that diffusion via invitations on LinkedIn occurs over much longer timescales than are typically associated with other types of online diffusion. Finally, we connect the cascade structures with rich individual-level attribute data to investigate the interplay between the two. Using novel techniques to study the role of homophily in diffusion, we find striking differences between the local, edge-wise homophily and the global, cascade-level homophily we observe in our data, suggesting that signup cascades form surprisingly coherent groups of members.

Organizational overlap on social networks and its applications

Online social networks have become important for networking, communication, sharing, and discovery. A considerable challenge these networks face is the fact that an online social network is partially observed because two individuals might know each other, but may not have established a connection on the site. Therefore, link prediction and recommendations are important tasks for any online social network. In this paper, we address the problem of computing edge affinity between two users on a social network, based on the users belonging to organizations such as companies, schools, and online groups. We present experimental insights from social network data on organizational overlap, a novel mathematical model to compute the probability of connection between two people based on organizational overlap, and experimental validation of this model based on real social network data. We also present novel ways in which the organization overlap model can be applied to link prediction and community detection, which in itself could be useful for recommending entities to follow and generating personalized news feed.

Large-scale social recommender systems: challenges and opportunities

Online social networks have become very important for networking, communication, sharing, and content discovery. Recommender systems play a significant role on any online social network for engaging members, recruiting new members, and recommending other members to connect with. This talk presents challenges in recommender systems, graph analysis, social stream relevance and virality on a large-scale social networks such as LinkedIn, the largest professional network with more than 200M members. First, social recommender systems for recommending jobs, groups, companies to follow, other members to connect with, are very important part of a professional network like LinkedIn [1, 6, 7, 9]. Each one of these entity recommender systems present novel challenges to use social and member generated data. Second, various problems, such as, link prediction, visualizing connection network, finding the strength of each connection, and the best path among members, require large-scale social graph analysis, and present unique research opportunities [2, 5]. Third, social stream relevance and capturing virality in social products are crucial for engaging users on any online social network [4]. Final, systems challenges must be addressed in scaling recommender systems on a large-scale social networks [3, 8, 10]. This talk presents challenges and interesting problems in large-scale social recommender systems, and describes some of the solutions.

Social networking in developing regions

Online social networks have enjoyed significant growth over the past several years. With improvements in mobile and Internet penetration, developing countries are participating in increasing numbers in online communities. This paper provides the first large scale and detailed analysis of social networking usage in developing country contexts. The analysis is based on data from LinkedIn, a professional social network with over 120 million members worldwide. LinkedIn has members from every country in the world, including millions in Africa, Asia, and South America. The goal of this paper is to provide researchers a detailed look at the growth, adoption, and other characteristics of social networking usage in developing countries compared to the developed world. To this end, we discuss several themes that illustrate different dimensions of social networking use, ranging from interconnectedness of members in geographic regions to the impact of local languages on social network participation.

Dynamics of Large Multi-View Social Networks: Synergy, Cannibalization and Cross-View Interplay

Most social networking services support multiple types of relationships between users, such as getting connected, sending messages, and consuming feed updates. These users and relationships can be naturally represented as a dynamic multi-view network, which is a set of weighted graphs with shared common nodes but having their own respective edges. Different network views, representing structural relationship and interaction types, could have very distinctive properties individually and these properties may change due to interplay across views. Therefore, it is of interest to study how multiple views interact and affect network dynamics and, in addition, explore possible applications to social networking. In this paper, we propose approaches to capture and analyze multi-view network dynamics from various aspects. Through our proposed descriptors, we observe the synergy and cannibalization between different user groups and network views from LinkedIn dataset. We then develop models that consider the synergy and cannibalization per new relationship, and show the outperforming predictive capability of our models compared to baseline models. Finally, the proposed models allow us to understand the interplay among different views where they dynamically change over time.

Importance of First Steps in a Community for Growth, Diversity, and Engagement

In both the online and offline world, networks that people form within certain communities are critical for their engagement and growth in those communities. In this work, we analyze the growth of ego-networks on LinkedIn for new employees of companies, and study how the pattern of network formation in the new company affects ones growth and engagement in that company. We observe that the initial state of ego-network growth in a newly joined company shows strong correlations with the future status in the company -- such as network size, network diversity, and retention. We also present some key patterns that demonstrate the importance of the first few connections in the new company as well as how they lead to the phenomena we observed.