Enumerating all simple paths in a graph

Rubin, Frank

doi:10.1109/tcs.1978.1084515

Cited by 47 publications

(18 citation statements)

References 4 publications

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“…Constructing functional dependency graph of a given schema is a straightforward task and its time complexity is O( F + ) which is negligable for a proactive process. Additionally, the time complexity of generating join chains for given "Required" or "Forbidden" sets mainly depends on the, "all simple paths" [21] algorithm, which corresponds to the main step of the process. Hence, also the overall algorithm is related to time complexity of DFS which is also negligible for a proactive solution.…”

Section: Methodsmentioning

confidence: 99%

Secure logical schema and decomposition algorithm for proactive context dependent attribute based inference control

Turan

Toroslu

Kantarcıoğlu

2017

Data & Knowledge Engineering

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Section: Methodsmentioning

confidence: 99%

Secure logical schema and decomposition algorithm for proactive context dependent attribute based inference control

Turan

Toroslu

Kantarcıoğlu

2017

Data & Knowledge Engineering

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“…Friendlink computes node similarity between any two nodes in a graph G. The initial input of Friendlink is the number n of nodes of G, the adjacency matrix A, and the length ℓ of paths that will be explored in G. To enumerate all simple paths in G, Rubin's algorithm [10] can be employed. However, Rubin's algorithm uses O(n 3 ) matrix operations to find all paths of different length between any pair of nodes.…”

Section: A the Friendlink Algorithmmentioning

confidence: 99%

Scalable Link Prediction in Social Networks Based on Local Graph Characteristics

Papadimitriou

Symeonidis

Manolopoulos

2012

2012 Ninth International Conference on Information Technology - New Generations

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Abstract-Online social networks (OSNs) like Facebook, Myspace, and Hi5 have become popular, because they allow users to easily share content or expand their social circle. OSNs recommend new friends to registered users based on local graph features (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a bounded length, based on the "algorithmic small world hypothesis". As a result, we are able to provide more accurate and faster friend recommendations. We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using two real data sets (Hi5 and Epinions). Our experimental results show that our FriendLink algorithm outperforms other approaches in terms of effectiveness and efficiency in all data sets. Finally, we discuss extensively various experimental considerations, such as a possible MapReduce implementation of FriendLink algorithm to achieve scalability.

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“…Friendlink computes node similarity between any two nodes in a graph G. The initial input of Friendlink is the number n of nodes of G, the adjacency matrix A, and the length ℓ of paths that will be explored in G. To enumerate all simple paths in G, Rubin's algorithm [8] can be employed. However, Rubin's algorithm uses O(n 3 ) matrix operations to find all paths of different length between any pair of nodes, where n is the number of nodes in G. In the following, we customize Rubin's algorithm to create only paths of length up to ℓ for our purpose.…”

Section: The Friendlink Algorithmmentioning

confidence: 99%

Geo-social recommendations based on incremental tensor reduction and local path traversal

Symeonidis

Papadimitriou

Manolopoulos

et al. 2011

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks

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Social networks have evolved with the combination of geographical data, into Geo-social networks (GSNs). GSNs give users the opportunity, not only to communicate with each other, but also to share images, videos, locations, and activities. The latest developments in GSNs incorporate the usage of location tracking services, such as GPS to allow users to "check in" at various locations and record their experience. In particular, users submit ratings or personal comments for their location/activity. The vast amount of data that is being generated by users with GPS devices, such as mobile phones, needs efficient methods for its effective management. In this paper, we have implemented an online prototype system, called Geo-social recommender system, where users can get recommendations on friends, locations and activities. For the friend recommendation task, we apply the FriendLink algorithm, which performs a local path traversal on the friendship network. In order to provide location/activity recommendations, we represent data by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. As more data is accumulated to the system, we use incremental solutions to update our tensor. We perform an experimental evaluation of our method with two real data sets and measure its effectiveness through recall/precision.

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Enumerating all simple paths in a graph

Cited by 47 publications

References 4 publications

Secure logical schema and decomposition algorithm for proactive context dependent attribute based inference control

Secure logical schema and decomposition algorithm for proactive context dependent attribute based inference control

Scalable Link Prediction in Social Networks Based on Local Graph Characteristics

Geo-social recommendations based on incremental tensor reduction and local path traversal

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