Social networks form a major parts of people’s lives, and individuals often make important life decisions based on information that spreads through these networks. For this reason, it is important to know whether individuals from different protected groups have equal access to information flowing through a network. In this article, we define theInformation Unfairness (IUF)metric, which quantifies inequality in access to information across protected groups. We then introduceMinIUF, an algorithm for reducing inequalities in information flow by adding edges to the network. Finally, we provide an in-depth analysis of information flow with respect to an attribute of interest, such as gender, across different types of networks to evaluate whether the structure of these networks allows groups to equally access information flowing in the network. Moreover, we investigate the causes of unfairness in such networks and how it can be improved.

Social networks play a vital role in the spread of information through a population, and individuals in networks make important life decisions on the basis of the information to which they have access. In many cases, it is important to evaluate whether information is spreading fairly to all groups in a network. For instance, are male and female students equally likely to hear about a new scholarship? In this paper, we present the information unfairness criterion, which measures whether information spreads fairly to all groups in a network. We perform a thorough case study on the DBLP computer science co-authorship network with respect to gender. We then propose MaxFair, an algorithm to add edges to a network to decrease information unfairness, and evaluate on several real-world network datasets.

While node semantics have been extensively explored in social networks, little research attention has been paid to pro le edge semantics, i.e., social relations. Ideal edge semantics should not only show that two users are connected, but also why they know each other and what they share in common. However, relations in social networks are often hard to pro le, due to noisy multi-modal signals and limited user-generated ground-truth labels.

Text-rich heterogeneous information networks (text-rich HINs) are ubiquitous in real-world applications. Hypernymy, also known as is-a relation or subclass-of relation, lays in the core of many knowledge graphs and benefits many downstream applications. Existing methods of hypernymy discovery either leverage textual patterns to extract explicitly mentioned hypernym-hyponym pairs, or learn a distributional representation for each term of interest based its context. These approaches rely on statistical signals from the textual corpus, and their effectiveness would therefore be hindered when the signals from the corpus are not sufficient for all terms of interest. In this work, we propose to discover hypernymy in text-rich HINs, which can introduce additional high-quality signals. We develop a new framework, named HyperMine, that exploits multi-granular contexts and combines signals from both text and network without human labeled data. HyperMine extends the definition of "context" to the scenario of text-rich HIN. For example, we can define typed nodes and communities as contexts. These contexts encode signals of different granularities and we feed them into a hypernymy inference model. HyperMine learns this model using weak supervision acquired based on high-precision textual patterns. Extensive experiments on two large real-world datasets demonstrate the effectiveness of HyperMine and the utility of modeling context granularity. We further show a case study that a high-quality taxonomy can be generated solely based on the hypernymy discovered by HyperMine.

Given a terabyte-scale graph distributed across multiple machines, how can we summarize it, with much fewer nodes and edges, so that we can restore the original graph exactly or within error bounds?

User engagement is a key factor for the success of web services. Studying the following questions will help establishing business strategies leading to their success: How do the behaviors of users in a web service evolve over time? To reach a certain engagement level, what are the common stages that many users go through? How can we represent the stage that each individual user lies in? To answer these questions, we propose a behavior model that discovers the progressions of users' behaviors from a given starting point - such as a new subscription or first experience of certain features - to a particular target stage such as a predefined engagement level of interest. Under our model, transitions over stages represent progression of users where each stage in our model is characterized by probability distributions over types of actions, frequencies of actions, and next stages to move. Each user performs actions and moves to a next stage following the probability distributions characterizing the current stage. We also develop a fast and memory-efficient algorithm that fits our model to trillions of behavioral logs. Our algorithm scales linearly with the size of data. Especially, its distributed version implemented in the MapReduce framework successfully handles petabyte-scale data with one trillion actions. Lastly, we show the effectiveness of our model and algorithm by applying them to real-world data from LinkedIn. We discover meaningful stages that LinkedIn users go through leading to predefined target goals. In addition, our trained models are shown to be useful for downstream tasks such as prediction of future actions.

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.

Relational data—like graphs, networks, and matrices—is often dynamic, where the relational structure evolves over time. A fundamental problem in the analysis of time-varying network data is to extract a summary of the common structure and the dynamics of underlying relations between entities. Here we build on the intuition that changes in the network structure are driven by the dynamics at the level of groups of nodes. We propose a nonparametric multi-group membership model for dynamic networks. Our model contains three main components. We model the birth and death of groups with respect to the dynamics of the network structure via a distance dependent Indian Buffet Process. We capture the evolution of individual node group memberships via a Factorial Hidden Markov model. And, we explain the dynamics of the network structure by explicitly modeling the connectivity structure. We demonstrate our model’s capability of identifying the dynamics of latent groups in a number of different types of network data. Experimental results show our model achieves higher predictive performance on the future network forecasting and missing link prediction.

Large-scale websites are predominantly built as a service-oriented architecture. Here, services are specialized for a certain task, run on multiple machines, and communicate with each other to serve a user's request. An anomalous change in a metric of one service can propagate to other services during this communication, resulting in overall degradation of the request. As any such degradation is revenue impacting, maintaining correct functionality is of paramount concern: it is important to find the root cause of any anomaly as quickly as possible. This is challenging because there are numerous metrics or sensors for a given service, and a modern website is usually composed of hundreds of services running on thousands of machines in multiple data centers.

While the social and information networks have become ubiquitous, the challenge of collecting complete network data still persists. Many times the collected network data is incomplete with nodes and edges missing. Commonly, only a part of the network can be observed and we would like to infer the unobserved part of the network. We address this issue by studying the Network Completion Problem: Given a network with missing nodes and edges, can we complete the missing part? We cast the problem in the Expectation Maximization (EM) framework where we use the observed part of the network to fit a model of network structure, and then we estimate the missing part of the network using the model, re-estimate the parameters and so on. We combine the EM algorithm with the Kronecker graphs model and design a scalable Metropolized Gibbs sampling approach that allows for the estimation of the model parameters as well as the inference about missing nodes and edges of the network. Experiments on synthetic and several real-world networks show that our approach can effectively recover the network even when about half of the nodes in the network are missing. Our algorithm outperforms not only classical link-prediction approaches but also the state of the art Stochastic block modeling approach. Furthermore, our algorithm easily scales to networks with tens of thousands of nodes.