3 research outputs found
Time Constrained Continuous Subgraph Search over Streaming Graphs
The growing popularity of dynamic applications such as social networks
provides a promising way to detect valuable information in real time. Efficient
analysis over high-speed data from dynamic applications is of great
significance. Data from these dynamic applications can be easily modeled as
streaming graph. In this paper, we study the subgraph (isomorphism) search over
streaming graph data that obeys timing order constraints over the occurrence of
edges in the stream. We propose a data structure and algorithm to efficiently
answer subgraph search and introduce optimizations to greatly reduce the space
cost, and propose concurrency management to improve system throughput.
Extensive experiments on real network traffic data and synthetic social
streaming data confirms the efficiency and effectiveness of our solution
Dolha - an Efficient and Exact Data Structure for Streaming Graphs
A streaming graph is a graph formed by a sequence of incoming edges with time
stamps. Unlike static graphs, the streaming graph is highly dynamic and time
related. In the real world, the high volume and velocity streaming graphs such
as internet traffic data, social network communication data and financial
transfer data are bringing challenges to the classic graph data structures. We
present a new data structure: double orthogonal list in hash table (Dolha)
which is a high speed and high memory efficiency graph structure applicable to
streaming graph. Dolha has constant time cost for single edge and near linear
space cost that we can contain billions of edges information in memory size and
process an incoming edge in nanoseconds. Dolha also has linear time cost for
neighborhood queries, which allow it to support most algorithms in graphs
without extra cost. We also present a persistent structure based on Dolha that
has the ability to handle the sliding window update and time related queries.Comment: 12 page
Updates-Aware Graph Pattern based Node Matching
Graph Pattern based Node Matching (GPNM) is to find all the matches of the
nodes in a data graph GD based on a given pattern graph GP. GPNM has become
increasingly important in many applications, e.g., group finding and expert
recommendation. In real scenarios, both GP and GD are updated frequently.
However, the existing GPNM methods either need to perform a new GPNM procedure
from scratch to deliver the node matching results based on the updated GP and
GD or incrementally perform the GPNM procedure for each of the updates, leading
to low efficiency. Therefore, there is a pressing need for a new method to
efficiently deliver the node matching results on the updated graphs. In this
paper, we first analyze and detect the elimination relationships between the
updates. Then, we construct an Elimination Hierarchy Tree (EH-Tree) to index
these elimination relationships. In order to speed up the GPNM process, we
propose a graph partition method and then propose a new updates-aware GPNM
method, called UA-GPNM, considering the single-graph elimination relationships
among the updates in a single graph of GP or GD, and also the cross-graph
elimination relationships between the updates in GP and the updates in GD.
UA-GPNM first delivers the GPNM result of an initial query, and then delivers
the GPNM result of a subsequent query, based on the initial GPNM result and the
multiple updates that occur between two queries. The experimental results on
five real-world social graphs demonstrate that our proposed UA-GPNM is much
more efficient than the state-of-the-art GPNM methods