37 research outputs found
Efficient Enumeration of Induced Subtrees in a K-Degenerate Graph
In this paper, we address the problem of enumerating all induced subtrees in
an input k-degenerate graph, where an induced subtree is an acyclic and
connected induced subgraph. A graph G = (V, E) is a k-degenerate graph if for
any its induced subgraph has a vertex whose degree is less than or equal to k,
and many real-world graphs have small degeneracies, or very close to small
degeneracies. Although, the studies are on subgraphs enumeration, such as
trees, paths, and matchings, but the problem addresses the subgraph
enumeration, such as enumeration of subgraphs that are trees. Their induced
subgraph versions have not been studied well. One of few example is for
chordless paths and cycles. Our motivation is to reduce the time complexity
close to O(1) for each solution. This type of optimal algorithms are proposed
many subgraph classes such as trees, and spanning trees. Induced subtrees are
fundamental object thus it should be studied deeply and there possibly exist
some efficient algorithms. Our algorithm utilizes nice properties of
k-degeneracy to state an effective amortized analysis. As a result, the time
complexity is reduced to O(k) time per induced subtree. The problem is solved
in constant time for each in planar graphs, as a corollary
Circle Feature Graphormer: Can Circle Features Stimulate Graph Transformer?
In this paper, we introduce two local graph features for missing link
prediction tasks on ogbl-citation2. We define the features as Circle Features,
which are borrowed from the concept of circle of friends. We propose the
detailed computing formulas for the above features. Firstly, we define the
first circle feature as modified swing for common graph, which comes from
bipartite graph. Secondly, we define the second circle feature as bridge, which
indicates the importance of two nodes for different circle of friends. In
addition, we firstly propose the above features as bias to enhance graph
transformer neural network, such that graph self-attention mechanism can be
improved. We implement a Circled Feature aware Graph transformer (CFG) model
based on SIEG network, which utilizes a double tower structure to capture both
global and local structure features. Experimental results show that CFG
achieves the state-of-the-art performance on dataset ogbl-citation2.Comment: 3 pages, 2 figures, 1 table, 31 references, manuscript in preparatio
An Efficient Algorithm for Enumerating Chordless Cycles and Chordless Paths
A chordless cycle (induced cycle) of a graph is a cycle without any
chord, meaning that there is no edge outside the cycle connecting two vertices
of the cycle. A chordless path is defined similarly. In this paper, we consider
the problems of enumerating chordless cycles/paths of a given graph
and propose algorithms taking time for each chordless cycle/path. In
the existing studies, the problems had not been deeply studied in the
theoretical computer science area, and no output polynomial time algorithm has
been proposed. Our experiments showed that the computation time of our
algorithms is constant per chordless cycle/path for non-dense random graphs and
real-world graphs. They also show that the number of chordless cycles is much
smaller than the number of cycles. We applied the algorithm to prediction of
NMR (Nuclear Magnetic Resonance) spectra, and increased the accuracy of the
prediction
Belief Propagation Min-Sum Algorithm for Generalized Min-Cost Network Flow
Belief Propagation algorithms are instruments used broadly to solve graphical
model optimization and statistical inference problems. In the general case of a
loopy Graphical Model, Belief Propagation is a heuristic which is quite
successful in practice, even though its empirical success, typically, lacks
theoretical guarantees. This paper extends the short list of special cases
where correctness and/or convergence of a Belief Propagation algorithm is
proven. We generalize formulation of Min-Sum Network Flow problem by relaxing
the flow conservation (balance) constraints and then proving that the Belief
Propagation algorithm converges to the exact result
Efficiently listing bounded length st-paths
The problem of listing the shortest simple (loopless) -paths in a
graph has been studied since the early 1960s. For a non-negatively weighted
graph with vertices and edges, the most efficient solution is an
algorithm for directed graphs by Yen and Lawler
[Management Science, 1971 and 1972], and an algorithm for
the undirected version by Katoh et al. [Networks, 1982], both using
space. In this work, we consider a different parameterization for this problem:
instead of bounding the number of -paths output, we bound their length. For
the bounded length parameterization, we propose new non-trivial algorithms
matching the time complexity of the classic algorithms but using only
space. Moreover, we provide a unified framework such that the solutions to both
parameterizations -- the classic -shortest and the new length-bounded paths
-- can be seen as two different traversals of a same tree, a Dijkstra-like and
a DFS-like traversal, respectively.Comment: 12 pages, accepted to IWOCA 201
Efficient Enumeration of Bipartite Subgraphs in Graphs
Subgraph enumeration problems ask to output all subgraphs of an input graph
that belongs to the specified graph class or satisfy the given constraint.
These problems have been widely studied in theoretical computer science. As
far, many efficient enumeration algorithms for the fundamental substructures
such as spanning trees, cycles, and paths, have been developed. This paper
addresses the enumeration problem of bipartite subgraphs. Even though bipartite
graphs are quite fundamental and have numerous applications in both theory and
application, its enumeration algorithms have not been intensively studied, to
the best of our knowledge. We propose the first non-trivial algorithms for
enumerating all bipartite subgraphs in a given graph. As the main results, we
develop two efficient algorithms: the one enumerates all bipartite induced
subgraphs of a graph with degeneracy in time per solution. The other
enumerates all bipartite subgraphs in time per solution
Enumerating All Subgraphs Under Given Constraints Using Zero-Suppressed Sentential Decision Diagrams
Subgraph enumeration is a fundamental task in computer science. Since the number of subgraphs can be large, some enumeration algorithms exploit compressed representations for efficiency. One such representation is the Zero-suppressed Binary Decision Diagram (ZDD). ZDDs can represent the set of subgraphs compactly and support several poly-time queries, such as counting and random sampling. Researchers have proposed efficient algorithms to construct ZDDs representing the set of subgraphs under several constraints, which yield fruitful results in many applications. Recently, Zero-suppressed Sentential Decision Diagrams (ZSDDs) have been proposed as variants of ZDDs. ZSDDs can be smaller than ZDDs when representing the same set of subgraphs. However, efficient algorithms to construct ZSDDs are known only for specific types of subgraphs: matchings and paths.
We propose a novel framework to construct ZSDDs representing sets of subgraphs under given constraints. Using our framework, we can construct ZSDDs representing several sets of subgraphs such as matchings, paths, cycles, and spanning trees. We show the bound of sizes of constructed ZSDDs by the branch-width of the input graph, which is smaller than that of ZDDs by the path-width. Experiments show that our methods can construct ZSDDs faster than ZDDs and that the constructed ZSDDs are smaller than ZDDs when representing the same set of subgraphs