In this article, we consider the problem of crawling a multiplex network to identify the community structure of a layer-of-interest. A multiplex network is one where there are multiple types of relationships between the nodes. In many multiplex networks, some layers might be easier to explore (in terms of time, money etc.). We propose MCS+ , an algorithm that can use the information from the easier to explore layers to help in the exploration of a layer-of-interest that is expensive to explore. We consider the goal of exploration to be generating a sample that is representative of the communities in the complete layer-of-interest. This work has practical applications in areas such as exploration of dark (e.g., criminal) networks, online social networks, biological networks, and so on. For example, in a terrorist network, relationships such as phone records, e-mail records, and so on are easier to collect; in contrast, data on the face-to-face communications are much harder to collect, but also potentially more valuable. We perform extensive experimental evaluations on real-world networks, and we observe that MCS+ consistently outperforms the best baseline—the similarity of the sample that MCS+ generates to the real network is up to three times that of the best baseline in some networks. We also perform theoretical and experimental evaluations on the scalability of MCS+ to network properties, and find that it scales well with the budget, number of layers in the multiplex network, and the average degree in the original network.

We examine the problem of crawling the community structure of a multiplex network containing multiple layers of edge relationships. While there has been a great deal of work examining community structure in general, and some work on the problem of sampling a network to preserve its community structure, to the best of our knowledge, this is the first work to consider this problem on multiplex networks. We consider the specific case in which the layers of a multiplex network have different query (collection) costs and reliabilities; and a data collector is interested in identifying the community structure of the most expensive layer. We propose MultiComSample (MCS), a novel algorithm for crawling a multiplex network. MCS uses multiple levels of multi-armed bandits to determine the best layers, communities and node roles for selecting nodes to query. We test MCS against six baseline algorithms on real-world multiplex networks, and achieved large gains in performance. For example, after consuming a budget equivalent to sampling 20% of the nodes in the expensive layer, we observe that MCS outperforms the best baseline by up to 49%.

We apply transfer learning techniques to create topically and/or stylistically biased natural language models from small data samples, given generic long short-term memory (LSTM) language models trained on larger data sets. Although LSTM language models are powerful tools with wide-ranging applications, they require enormous amounts of data and time to train. Thus, we build general purpose language models that take advantage of large standing corpora and computational resources proactively, allowing us to build more specialized analytical tools from smaller data sets on demand. We show that it is possible to construct a language model from a small, focused corpus by first training an LSTM language model on a large corpus (e.g., the text from English Wikipedia) and then retraining only the internal transition model parameters on the smaller corpus. We also show that a single general language model can be reused through transfer learning to create many distinct special purpose language models quickly with modest amounts of data.

We present a temporal model of individual-scale social media user behavior, comprising modal activity levels and mode switching patterns. We show that this model can be effectively and easily learned from available social media data, and that our model is sufficiently flexible to capture diverse users' daily activity patterns. In applications such as electric power load prediction, computer network traffic analysis, disease spread modeling, and disease outbreak forecasting, it is useful to have a model of individual-scale patterns of human behavior. Our user model is intended to be suitable for integration into such population models, for future applications of prediction, change detection, or agent-based simulation.

We present a detailed study on data collection, graph construction, and sampling in Twitter. We observe that sampling on semantic graphs (i.e., graphs with multiple edge types) presents fundamentally distinct challenges from sampling on traditional graphs. The purpose of our work is to present new challenges and initial solutions for sampling semantic graphs. Novel elements of our work include the following: (1) We provide a thorough discussion of problems encountered with naïve breadth-first search on semantic graphs. We argue that common sampling methods such as breadth-first search face specific challenges on semantic graphs that are not encountered on graphs with homogeneous edge types. (2) We present two competing methods for creating semantic graphs from data collects, corresponding to the interactions between sampling of different edge types. (3) We discuss new metrics specific to graphs with multiple edge types, and discuss the effect of the sampling method on these metrics. (4) We discuss issues and potential solutions pertaining to sampling semantic graphs.

Head motion during real walking is complex: The basic translational path is obscured by head bobbing. Many VE applications would be improved if a bobbing-free path were available. This paper introduces a model that describes head position while walking in terms of a bobbing free path and the head bobs. We introduce two methods to approximate the model from head-track data.

Many Virtual Environments require walking interfaces to explore virtual worlds much larger than available real-world tracked space. We present a model for generating virtual locomotion speeds from Walking-In-Place (WIP) inputs based on walking biomechanics. By employing gait principles, our model - called Gait-Understanding-Driven Walking-In-Place (GUD WIP) - creates output speeds which better match those evident in Real Walking, and which better respond to variations in step frequency, including realistic starting and stopping. The speeds output by our implementation demonstrate considerably less within-step fluctuation than a good current WIP system - Low-Latency, Continuous-Motion (LLCM) WIP - while still remaining responsive to changes in user input. We compared resulting speeds from Real Walking, GUD WIP, and LLCM-WIP via user study: The average output speeds for Real Walking and GUD WIP respond consistently with changing step frequency - LLCM-WIP is far less consistent. GUD WIP produces output speeds that are more locally consistent (smooth) and step-frequency-to-walk-speed consistent than LLCM-WIP.