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Models of human navigation in information networks based
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Analyzing Expert Behaviors in Collaborative Networks
"... Collaborative networks are composed of experts who coop-erate with each other to complete specific tasks, such as resolving problems reported by customers. A task is posted and subsequently routed in the network from an expert to another until being resolved. When an expert cannot solve a task, his ..."
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Collaborative networks are composed of experts who coop-erate with each other to complete specific tasks, such as resolving problems reported by customers. A task is posted and subsequently routed in the network from an expert to another until being resolved. When an expert cannot solve a task, his routing decision (i.e., where to transfer a task) is critical since it can significantly affect the completion time of a task. In this work, we attempt to deduce the cognitive process of task routing, and model the decision making of experts as a generative process where a routing decision is made based on mixed routing patterns. In particular, we observe an interesting phenomenon that an expert tends to transfer a task to someone whose knowl-edge is neither too similar to nor too different from his own. Based on this observation, an expertise difference based rout-ing pattern is developed. We formalize multiple routing patterns by taking into account both rational and random analysis of tasks, and present a generative model to com-bine them. For a held-out set of tasks, our model not only explains their real routing sequences very well, but also accu-rately predicts their completion time. Under three different quality measures, our method significantly outperforms al-l the alternatives with more than 75 % accuracy gain. In practice, with the help of our model, hypotheses on how to improve a collaborative network can be tested quickly and reliably, thereby significantly easing performance improve-ment of collaborative networks.
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"... IOS Press Using ontologies to model human navigation behavior in information networks: A study based on Wikipedia ..."
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IOS Press Using ontologies to model human navigation behavior in information networks: A study based on Wikipedia
Expertise-Based Data Access in Content-Centric Mobile Opportunistic Networks
"... Abstract—In mobile opportunistic networks, most existing research focuses on how to choose appropriate relays to carry and forward data. Although relay selection is an important issue, other issues such as finding content from people with the right expertise are also very important since the ultimat ..."
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Abstract—In mobile opportunistic networks, most existing research focuses on how to choose appropriate relays to carry and forward data. Although relay selection is an important issue, other issues such as finding content from people with the right expertise are also very important since the ultimate goal of using mobile opportunistic network is to provide the right content to mobile users (nodes). In this paper, we study expertise-based data access in content-centric mobile opportunistic networks, where the objective is to minimize the average query delay given a sequence of queries considering node expertise, node queuing delay and communication delay. To solve this problem, we propose various query forwarding approaches under determin-istic and probabilistic expertise models. Specifically, we propose centralized approaches to assign queries based on a modified Dijkstra’s shortest path algorithm and distributed approaches in which query forwarding is based on a utility metric. Extensive simulations on both synthetic and realistic traces demonstrate that our solutions outperform existing approaches. I.
Novel Metrics for Bug Triage
"... Abstract—Bug Triaging is a vital part of issue management systems. Bug triaging deals with assigning a developer the task of an incoming bug. This activity is error prone and time consuming if done manually. There is a need for automated support to accelerate this process. The current automated bug ..."
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Abstract—Bug Triaging is a vital part of issue management systems. Bug triaging deals with assigning a developer the task of an incoming bug. This activity is error prone and time consuming if done manually. There is a need for automated support to accelerate this process. The current automated bug triaging systems exploits the text contents of the bug and the tossing relations among the developers. The automated bug triaging systems estimate the optimal bath between the first assignee of the bug and the bug resolver using the tossing relations. The metrics used for assessing the efficiency of bug triaging systems that are based on tossing relations is Mean number of Steps To Resolve (MSTR). This metric quantifies the number of steps reduced by the predicted path compared to the original path. It does not capture how far the retrieved path is in alignment with the actual path. MSTR does reveal the information regarding the extent to which the order of the developers in the retrieved path is in line with that of the original path. In addition, there are no indicators for measuring the strength of the retrieved path. In this paper, we propose two metrics (i) Path Similarity Metric which quantifies path alignment based on pair wise path alignment and (ii) Path Alignment Indicator that measures the effectiveness of the retrieved path based on degree centrality. The effectiveness of the two proposed metrics is validated using bug reports extracted from the Eclipse project.
