138,303 research outputs found

    Benchmarking Measures of Network Influence

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    Identifying key agents for the transmission of diseases (ideas, technology, etc.) across social networks has predominantly relied on measures of centrality on a static base network or a temporally flattened graph of agent interactions. Various measures have been proposed as the best trackers of influence, such as degree centrality, betweenness, and kk-shell, depending on the structure of the connectivity. We consider SIR and SIS propagation dynamics on a temporally-extruded network of observed interactions and measure the conditional marginal spread as the change in the magnitude of the infection given the removal of each agent at each time: its temporal knockout (TKO) score. We argue that the exhaustive approach of the TKO score makes it an effective benchmark measure for evaluating the accuracy of other, often more practical, measures of influence. We find that none of the common network measures applied to the induced flat graphs are accurate predictors of network propagation influence on the systems studied; however, temporal networks and the TKO measure provide the requisite targets for the hunt for effective predictive measures

    Exploiting Query Structure and Document Structure to Improve Document Retrieval Effectiveness

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    In this paper we present a systematic analysis of document retrieval using unstructured and structured queries within the score region algebra (SRA) structured retrieval framework. The behavior of di®erent retrieval models, namely Boolean, tf.idf, GPX, language models, and Okapi, is tested using the transparent SRA framework in our three-level structured retrieval system called TIJAH. The retrieval models are implemented along four elementary retrieval aspects: element and term selection, element score computation, score combination, and score propagation. The analysis is performed on a numerous experiments evaluated on TREC and CLEF collections, using manually generated unstructured and structured queries. Unstructured queries range from the short title queries to long title + description + narrative queries. For generating structured queries we exploit the knowledge of the document structure and the content used to semantically describe or classify documents. We show that such structured information can be utilized in retrieval engines to give more precise answers to user queries then when using unstructured queries
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