3,346 research outputs found
Temporal Graph Traversals: Definitions, Algorithms, and Applications
A temporal graph is a graph in which connections between vertices are active
at specific times, and such temporal information leads to completely new
patterns and knowledge that are not present in a non-temporal graph. In this
paper, we study traversal problems in a temporal graph. Graph traversals, such
as DFS and BFS, are basic operations for processing and studying a graph. While
both DFS and BFS are well-known simple concepts, it is non-trivial to adopt the
same notions from a non-temporal graph to a temporal graph. We analyze the
difficulties of defining temporal graph traversals and propose new definitions
of DFS and BFS for a temporal graph. We investigate the properties of temporal
DFS and BFS, and propose efficient algorithms with optimal complexity. In
particular, we also study important applications of temporal DFS and BFS. We
verify the efficiency and importance of our graph traversal algorithms in real
world temporal graphs
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
Investigation of Database Models for Evolving Graphs
We deal with the efficient implementation of storage models for time-varying graphs. To this end, we present an improved approach for the HiNode vertex-centric model based on MongoDB. This approach, apart from its inherent space optimality, exhibits significant improvements in global query execution times, which is the most challenging query type for entity-centric approaches. Not only significant speedups are achieved but more expensive queries can be executed as well, when compared to an implementation based on Cassandra due to the capability to exploit indices to a larger extent and benefit from in-database query processing
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
Fast and simple connectivity in graph timelines
In this paper we study the problem of answering connectivity queries about a
\emph{graph timeline}. A graph timeline is a sequence of undirected graphs
on a common set of vertices of size such that each graph
is obtained from the previous one by an addition or a deletion of a single
edge. We present data structures, which preprocess the timeline and can answer
the following queries:
- forall -- does the path exist in each of
?
- exists -- does the path exist in any of
?
- forall2 -- do there exist two edge-disjoint paths connecting
and in each of
We show data structures that can answer forall and forall2 queries in time after preprocessing in time. Here by we denote the
number of edges that remain unchanged in each graph of the timeline. For the
case of exists queries, we show how to extend an existing data structure to
obtain a preprocessing/query trade-off of and show a matching conditional lower bound.Comment: 21 pages, extended abstract to appear in WADS'1
Sequence queries on temporal graphs
Graphs that evolve over time are called temporal graphs. They can be used to describe and represent real-world networks, including transportation networks, social networks, and communication networks, with higher fidelity and accuracy. However, research is still limited on how to manage large scale temporal graphs and execute queries over these graphs efficiently and effectively. This thesis investigates the problems of temporal graph data management related to node and edge sequence queries. In temporal graphs, nodes and edges can evolve over time. Therefore, sequence queries on nodes and edges can be key components in managing temporal graphs. In this thesis, the node sequence query decomposes into two parts: graph node similarity and subsequence matching. For node similarity, this thesis proposes a modified tree edit distance that is metric and polynomially computable and has a natural, intuitive interpretation. Note that the proposed node similarity works even for inter-graph nodes and therefore can be used for graph de-anonymization, network transfer learning, and cross-network mining, among other tasks. The subsequence matching query proposed in this thesis is a framework that can be adopted to index generic sequence and time-series data, including trajectory data and even DNA sequences for subsequence retrieval. For edge sequence queries, this thesis proposes an efficient storage and optimized indexing technique that allows for efficient retrieval of temporal subgraphs that satisfy certain temporal predicates. For this problem, this thesis develops a lightweight data management engine prototype that can support time-sensitive temporal graph analytics efficiently even on a single PC
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