Algorithm 447: efficient algorithms for graph manipulation

Hopcroft, John E.; Tarjan, Robert E.

doi:10.1145/362248.362272

Cited by 896 publications

(551 citation statements)

References 4 publications

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“…In our context, a high entropy value associated with a random walk means that there is a lot of information available for the user to possibly learn when browsing the graph. The specific entropy measures we use relate to the following sets of nodes: (D) abstract MeSH clusters, path only; (E) specific MeSH clusters, path only; (F) abstract MeSH clusters, path and envelope; (G) specific MeSH clusters, path and envelope; (H) clusters defined by biconnected components [Hopcroft and Tarjan, 1973] in the envelope 11 . The entropies of the sets (D-G) are defined by formulae (9-10) in Appendix A.3.…”

Section: Running and Measuring The Random Walksmentioning

confidence: 99%

SKIMMR: Facilitating knowledge discovery in life sciences by machine-aided skim reading

Nováček

Burns

2014

Preprint

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Background: Unlike full reading, 'skim-reading' involves the process of looking quickly over information in an attempt to cover more material whilst still being able to retain a superficial view of the underlying content. Within this work, we specifically emulate this natural human activity by providing a dynamic graph-based view of entities automatically extracted from text. For the extraction, we use shallow parsing, co-occurrence analysis and semantic similarity computation techniques. Our main motivation is to assist biomedical researchers and clinicians in coping with increasingly large amounts of potentially relevant articles that are being published ongoingly in life sciences. Methods: To construct the high-level network overview of articles, we extract weighted binary statements from the text. We consider two types of these statements, co-occurrence and similarity, both organised in the same distributional representation (i.e., in a vectorspace model). For the co-occurrence weights, we use point-wise mutual information that indicates the degree of non-random association between two co-occurring entities. For computing the similarity statement weights, we use cosine distance based on the relevant co-occurrence vectors. These statements are used to build fuzzy indices of terms, statements and provenance article identifiers, which support fuzzy querying and subsequent result ranking. These indexing and querying processes are then used to construct a graphbased interface for searching and browsing entity networks extracted from articles, as well as articles relevant to the networks being browsed. Last but not least, we describe a methodology for automated experimental evaluation of the presented approach. The method uses formal comparison of the graphs generated by our tool to relevant gold standards based on manually curated PubMed, TREC challenge and MeSH data. Results: We provide a web-based prototype (called 'SKIMMR') that generates a network of inter-related entities from a set of documents which a user may explore through our interface. When a particular area of the entity network looks interesting to a user, the tool displays the documents that are the most relevant to those entities of interest currently shown in the network. We present this as a methodology for browsing a collection of research articles. PrePrints SKIMMR: Machine-Aided Skim Reading 2 report on the results of experimental evaluation using the two domains and one additional dataset based on the TREC challenge. The results show that the presented method for machine-aided skim reading outperforms tools like PubMed regarding focused browsing and informativeness of the browsing context. Conclusions: Both conceptually and practically, the SKIMMR methodology instantiates the notion of 'skim reading' within software. In preliminary trials, sample users find new, interesting and non-trivial facts with the tool. A comprehensive experimental evaluation of the SKIMMR prototype using simulations of various types of browsing behaviour show...

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Section: Running and Measuring The Random Walksmentioning

confidence: 99%

SKIMMR: Facilitating knowledge discovery in life sciences by machine-aided skim reading

Nováček

Burns

2014

Preprint

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“…In graph theory, links with a bike lane should form a number of connected components that have at least one destination node (vertex). This constraint can be verified with graph theory methods such as breadth-first search or depth-first search (10) or, more specifically, with Dijkstra's shortest path algorithm (11). Computation of flow and travel time at the lower level is based on the set of decision variables in the upper level.…”

Section: Upper-level Formulationmentioning

confidence: 99%

Bilevel Optimization Approach to Design of Network of Bike Lanes

Mesbah

Thompson

Moridpour

2012

Transportation Research Record: Journal of the Transportation R

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A bike lane is an effective way to improve cycling safety and to decrease greenhouse gas emissions with the promotion of cycling. Improvements include high-quality off-road facilities and on-road bike lanes. Whereas construction of off-road lanes is not always possible because of urban land constraints and construction costs, on-road lanes can be a cost-effective alternative. An optimization framework for the design of a network of bike lanes in an urban road network was proposed. This framework identified links on which a bike lane could be introduced. Allocation of a lane to cyclists would increase the use of cycling, although it could disadvantage auto traffic. The presented approach balances the effects of a bike lane for all stakeholders. A bilevel optimization was proposed to encompass the benefits of cyclists and car users at the upper level and a model for traffic and bike demand assignment at the lower level. The objective function was defined by a weighted sum of a measure for private car users (total travel time) versus a measure for bike users (total travel distance on bike lanes). A genetic algorithm was developed to solve the bilevel formulation, which included introduction of a special crossover technique and a mutation technique. The proposed optimization will help transport authorities at the planning stage to quantify the outcomes of various strategies for active transport.

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“…Unlike finding the expansion coefficient of a graph, calculating the number of components can be done in linear time using depth-first search, as described in [15]. The global connectivity of a graph is the number of nodes in its largest component divided by the total number of nodes.…”

Section: Using Expansion As a Metricmentioning

confidence: 99%

On the Role of Expander Graphs in Key Predistribution Schemes for Wireless Sensor Networks

Kendall

Martin

2012

Research in Cryptology

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Abstract. Providing security for a wireless sensor network composed of small sensor nodes with limited battery power and memory can be a nontrivial task. A variety of key predistribution schemes have been proposed which allocate symmetric keys to the sensor nodes before deployment. In this paper we examine the role of expander graphs in key predistribution schemes for wireless sensor networks. Roughly speaking, a graph has good expansion if every 'small' subset of vertices has a 'large' neighbourhood, and intuitively, expansion is a desirable property for graphs of networks. It has been claimed that good expansion in the product graph is necessary for 'optimal' networks. We demonstrate flaws in this claim, argue instead that good expansion is desirable in the intersection graph, and discuss how this can be achieved. We then consider key predistribution schemes based on expander graph constructions and compare them to other schemes in the literature. Finally, we propose the use of expansion and other graph-theoretical techniques as metrics for assessing key predistribution schemes and their resulting wireless sensor networks.

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Algorithm 447: efficient algorithms for graph manipulation

Cited by 896 publications

References 4 publications

SKIMMR: Facilitating knowledge discovery in life sciences by machine-aided skim reading

SKIMMR: Facilitating knowledge discovery in life sciences by machine-aided skim reading

Bilevel Optimization Approach to Design of Network of Bike Lanes

On the Role of Expander Graphs in Key Predistribution Schemes for Wireless Sensor Networks

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