1,540 research outputs found
Top-L Most Influential Community Detection Over Social Networks (Technical Report)
In many real-world applications such as social network analysis and online
marketing/advertising, the community detection is a fundamental task to
identify communities (subgraphs) in social networks with high structural
cohesiveness. While previous works focus on detecting communities alone, they
do not consider the collective influences of users in these communities on
other user nodes in social networks. Inspired by this, in this paper, we
investigate the influence propagation from some seed communities and their
influential effects that result in the influenced communities. We propose a
novel problem, named Top-L most Influential Community DEtection (TopL-ICDE)
over social networks, which aims to retrieve top-L seed communities with the
highest influences, having high structural cohesiveness, and containing
user-specified query keywords. In order to efficiently tackle the TopL-ICDE
problem, we design effective pruning strategies to filter out false alarms of
seed communities and propose an effective index mechanism to facilitate
efficient Top-L community retrieval. We develop an efficient TopL-ICDE
answering algorithm by traversing the index and applying our proposed pruning
strategies. We also formulate and tackle a variant of TopL-ICDE, named
diversified top-L most influential community detection (DTopL-ICDE), which
returns a set of L diversified communities with the highest diversity score
(i.e., collaborative influences by L communities). We prove that DTopL-ICDE is
NP-hard, and propose an efficient greedy algorithm with our designed diversity
score pruning. Through extensive experiments, we verify the efficiency and
effectiveness of our proposed TopL-ICDE and DTopL-ICDE approaches over
real/synthetic social networks under various parameter settings
Fast Butterfly-Core Community Search For Large Labeled Graphs
Community Search (CS) aims to identify densely interconnected subgraphs
corresponding to query vertices within a graph. However, existing heterogeneous
graph-based community search methods need help identifying cross-group
communities and suffer from efficiency issues, making them unsuitable for large
graphs. This paper presents a fast community search model based on the
Butterfly-Core Community (BCC) structure for heterogeneous graphs. The Random
Walk with Restart (RWR) algorithm and butterfly degree comprehensively evaluate
the importance of vertices within communities, allowing leader vertices to be
rapidly updated to maintain cross-group cohesion. Moreover, we devised a more
efficient method for updating vertex distances, which minimizes vertex visits
and enhances operational efficiency. Extensive experiments on several
real-world temporal graphs demonstrate the effectiveness and efficiency of this
solution.Comment: 8 pages, 8 figure
Graph Contrastive Learning with Cohesive Subgraph Awareness
Graph contrastive learning (GCL) has emerged as a state-of-the-art strategy
for learning representations of diverse graphs including social and biomedical
networks. GCL widely uses stochastic graph topology augmentation, such as
uniform node dropping, to generate augmented graphs. However, such stochastic
augmentations may severely damage the intrinsic properties of a graph and
deteriorate the following representation learning process. We argue that
incorporating an awareness of cohesive subgraphs during the graph augmentation
and learning processes has the potential to enhance GCL performance. To this
end, we propose a novel unified framework called CTAug, to seamlessly integrate
cohesion awareness into various existing GCL mechanisms. In particular, CTAug
comprises two specialized modules: topology augmentation enhancement and graph
learning enhancement. The former module generates augmented graphs that
carefully preserve cohesion properties, while the latter module bolsters the
graph encoder's ability to discern subgraph patterns. Theoretical analysis
shows that CTAug can strictly improve existing GCL mechanisms. Empirical
experiments verify that CTAug can achieve state-of-the-art performance for
graph representation learning, especially for graphs with high degrees. The
code is available at https://doi.org/10.5281/zenodo.10594093, or
https://github.com/wuyucheng2002/CTAug
DMCS : Density Modularity based Community Search
Community Search, or finding a connected subgraph (known as a community)
containing the given query nodes in a social network, is a fundamental problem.
Most of the existing community search models only focus on the internal
cohesiveness of a community. However, a high-quality community often has high
modularity, which means dense connections inside communities and sparse
connections to the nodes outside the community. In this paper, we conduct a
pioneer study on searching a community with high modularity. We point out that
while modularity has been popularly used in community detection (without query
nodes), it has not been adopted for community search, surprisingly, and its
application in community search (related to query nodes) brings in new
challenges. We address these challenges by designing a new graph modularity
function named Density Modularity. To the best of our knowledge, this is the
first work on the community search problem using graph modularity. The
community search based on the density modularity, termed as DMCS, is to find a
community in a social network that contains all the query nodes and has high
density-modularity. We prove that the DMCS problem is NP-hard. To efficiently
address DMCS, we present new algorithms that run in log-linear time to the
graph size. We conduct extensive experimental studies in real-world and
synthetic networks, which offer insights into the efficiency and effectiveness
of our algorithms. In particular, our algorithm achieves up to 8.5 times higher
accuracy in terms of NMI than baseline algorithms
- …