1,779 research outputs found
Fast Search for Dynamic Multi-Relational Graphs
Acting on time-critical events by processing ever growing social media or
news streams is a major technical challenge. Many of these data sources can be
modeled as multi-relational graphs. Continuous queries or techniques to search
for rare events that typically arise in monitoring applications have been
studied extensively for relational databases. This work is dedicated to answer
the question that emerges naturally: how can we efficiently execute a
continuous query on a dynamic graph? This paper presents an exact subgraph
search algorithm that exploits the temporal characteristics of representative
queries for online news or social media monitoring. The algorithm is based on a
novel data structure called the Subgraph Join Tree (SJ-Tree) that leverages the
structural and semantic characteristics of the underlying multi-relational
graph. The paper concludes with extensive experimentation on several real-world
datasets that demonstrates the validity of this approach.Comment: SIGMOD Workshop on Dynamic Networks Management and Mining (DyNetMM),
201
Nowhere dense graph classes, stability, and the independence property
A class of graphs is nowhere dense if for every integer r there is a finite
upper bound on the size of cliques that occur as (topological) r-minors. We
observe that this tameness notion from algorithmic graph theory is essentially
the earlier stability theoretic notion of superflatness. For subgraph-closed
classes of graphs we prove equivalence to stability and to not having the
independence property.Comment: 9 page
Bowtie-free graphs and generic automorphisms
We show that the -categorical existentially closed universal
bowtie-free graph of Cherlin-Shelah-Shi admits generic automorphisms in the
sense of Truss. Moreover, we show that this graph is not finitely
homogenisable.Comment: 14 page
A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs
Cyber security is one of the most significant technical challenges in current
times. Detecting adversarial activities, prevention of theft of intellectual
properties and customer data is a high priority for corporations and government
agencies around the world. Cyber defenders need to analyze massive-scale,
high-resolution network flows to identify, categorize, and mitigate attacks
involving networks spanning institutional and national boundaries. Many of the
cyber attacks can be described as subgraph patterns, with prominent examples
being insider infiltrations (path queries), denial of service (parallel paths)
and malicious spreads (tree queries). This motivates us to explore subgraph
matching on streaming graphs in a continuous setting. The novelty of our work
lies in using the subgraph distributional statistics collected from the
streaming graph to determine the query processing strategy. We introduce a
"Lazy Search" algorithm where the search strategy is decided on a
vertex-to-vertex basis depending on the likelihood of a match in the vertex
neighborhood. We also propose a metric named "Relative Selectivity" that is
used to select between different query processing strategies. Our experiments
performed on real online news, network traffic stream and a synthetic social
network benchmark demonstrate 10-100x speedups over selectivity agnostic
approaches.Comment: in 18th International Conference on Extending Database Technology
(EDBT) (2015
Shrub-depth: Capturing Height of Dense Graphs
The recent increase of interest in the graph invariant called tree-depth and
in its applications in algorithms and logic on graphs led to a natural
question: is there an analogously useful "depth" notion also for dense graphs
(say; one which is stable under graph complementation)? To this end, in a 2012
conference paper, a new notion of shrub-depth has been introduced, such that it
is related to the established notion of clique-width in a similar way as
tree-depth is related to tree-width. Since then shrub-depth has been
successfully used in several research papers. Here we provide an in-depth
review of the definition and basic properties of shrub-depth, and we focus on
its logical aspects which turned out to be most useful. In particular, we use
shrub-depth to give a characterization of the lower levels of the
MSO1 transduction hierarchy of simple graphs
Inductive queries for a drug designing robot scientist
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
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