An important area of social network research is identifying missing information which is not explicitly represented in the network or is not visible to all. In this paper, we propose a novel Hybrid Approach of Classifier and Clustering,a which we refer to as HACC, to solve the missing node identification problem in social networks. HACC utilizes a classifier as a preprocessing step in order to integrate all known information into one similarity measure and then uses a clustering algorithm to identify missing nodes. Specifically, we used the information on the network structure, attributes about known users (nodes) and pictorial information to evaluate HACC and found that it performs significantly better than other missing node algorithms. We also argue that HACC is a general approach and domain independent and can be easily applied to other domains. We support this claim by evaluating HACC on a second authorship identification domain as well.

Scenario-based serious-games have become an important tool for teaching new skills and capabilities. An important factor in the development of such systems is reducing the time and cost overheads in manually creating content for these scenarios. To address this challenge, we present ScenarioGen, an automatic method for generating content about everyday activities through combining computer science techniques with the crowd. ScenarioGen uses the crowd in three different ways: to capture a database of scenarios of everyday activities, to generate a database of likely replacements for specific events within that scenario, and to evaluate the resulting scenarios. We evaluated ScenarioGen in 6 different content domains and found that it was consistently rated as coherent and consistent as the originally captured content. We also compared ScenarioGen's content to that created by traditional planning techniques. We found that both methods were equally effective in generating coherent and consistent scenarios, yet ScenarioGen's content was found to be more varied and easier to create.

An important area of social networks research is identifying missing information which is not explicitly represented in the network, or is not visible to all. Recently, the Missing Node Identification problem was introduced where missing members in the social network structure must be identified. However, previous works did not consider the possibility that information about specific users (nodes) within the network could be useful in solving this problem. In this paper, we present two algorithms: SAMI--A and SAMI--N. Both of these algorithms use the known nodes' specific information, such as demographic information and the nodes' historical behavior in the network. We found that both SAMI--A and SAMI--N perform significantly better than other missing node algorithms. However, as each of these algorithms and the parameters within these algorithms often perform better in specific problem instances, a mechanism is needed to select the best algorithm and the best variation within that algorithm. Towards this challenge, we also present OASCA, a novel online selection algorithm. We present results that detail the success of the algorithms presented within this paper.

In recent years, social networks have surged in popularity. One key aspect of social network research is identifying important missing information that is not explicitly represented in the network, or is not visible to all. To date, this line of research typically focused on finding the connections that are missing between nodes, a challenge typically termed as the link prediction problem . This article introduces the missing node identification problem, where missing members in the social network structure must be identified. In this problem, indications of missing nodes are assumed to exist. Given these indications and a partial network, we must assess which indications originate from the same missing node and determine the full network structure. Toward solving this problem, we present the missing node identification by spectral clustering algorithm (MISC), an approach based on a spectral clustering algorithm, combined with nodes’ pairwise affinity measures that were adopted from link prediction research. We evaluate the performance of our approach in different problem settings and scenarios, using real-life data from Facebook. The results show that our approach has beneficial results and can be effective in solving the missing node identification problem. In addition, this article also presents R-MISC, which uses a sparse matrix representation, efficient algorithms for calculating the nodes’ pairwise affinity, and a proprietary dimension reduction technique to enable scaling the MISC algorithm to large networks of more than 100,000 nodes. Last, we consider problem settings where some of the indications are unknown. Two algorithms are suggested for this problem: speculative MISC, based on MISC, and missing link completion, based on classical link prediction literature. We show that speculative MISC outperforms missing link completion.

In this paper, we address the tradeoff between exploration and exploitation for agents which need to learn more about the structure of their environment in order to perform more effectively. For example, a software agent operating on the World Wide Web may need to learn which sites on the net are most useful, and the most efficient routes to those sites. We compare exploration strategies for a repeated task, where the agent is given some particular task to perform some number of times. Tasks are modeled as navigation on a partially known (deterministic) graph. This paper describes a new utility-based exploration algorithm for repeated tasks which interleaves exploration with task performance. The method takes into account both the costs and the potential benefits (for future task repetitions) of different exploratory actions. Exploration is performed in a greedy fashion, with the locally optimal exploratory action performed during repetition of each task. We experimentally evaluated our utility-based interleaved exploration algorithm against a heuristic search algorithm for exploration before task performance (a priori exploration) as well as a randomized interleaved exploration algorithm. We found that for a single repeated task, utility-based interleaved exploration consistently outperforms the alternatives, unless the number of task repetitions is very high. In addition, we extended the algorithms for the case of multiple repeated tasks, where the agent has a different, randomly-chosen task (from a known subset of possible tasks) to perform each time. Here too, we found that utility-based interleaved exploration is clear in most cases.