5 research outputs found
VAIM: Visual Analytics for Influence Maximization
In social networks, individuals' decisions are strongly influenced by
recommendations from their friends and acquaintances. The influence
maximization (IM) problem asks to select a seed set of users that maximizes the
influence spread, i.e., the expected number of users influenced through a
stochastic diffusion process triggered by the seeds. In this paper, we present
VAIM, a visual analytics system that supports users in analyzing the
information diffusion process determined by different IM algorithms. By using
VAIM one can: (i) simulate the information spread for a given seed set on a
large network, (ii) analyze and compare the effectiveness of different seed
sets, and (iii) modify the seed sets to improve the corresponding influence
spread.Comment: Appears in the Proceedings of the 28th International Symposium on
Graph Drawing and Network Visualization (GD 2020
NetReAct: Interactive Learning for Network Summarization
Generating useful network summaries is a challenging and important problem
with several applications like sensemaking, visualization, and compression.
However, most of the current work in this space do not take human feedback into
account while generating summaries. Consider an intelligence analysis scenario,
where the analyst is exploring a similarity network between documents. The
analyst can express her agreement/disagreement with the visualization of the
network summary via iterative feedback, e.g. closing or moving documents
("nodes") together. How can we use this feedback to improve the network summary
quality? In this paper, we present NetReAct, a novel interactive network
summarization algorithm which supports the visualization of networks induced by
text corpora to perform sensemaking. NetReAct incorporates human feedback with
reinforcement learning to summarize and visualize document networks. Using
scenarios from two datasets, we show how NetReAct is successful in generating
high-quality summaries and visualizations that reveal hidden patterns better
than other non-trivial baselines.Comment: Presented at NeuRIPS 2020 HAMLET
Enabling efficient application monitoring in cloud data centers using SDN
Software Defined Networking (SDN) not only enables agility through the
realization of part of the network functionality in software but also
facilitates offering advanced features at the network layer. Hence, SDN can
support a wide range of middleware services; network performance monitoring is
an example of these services that are already deployed in practice. In this
paper, we exploit the use of SDNs to efficiently provide application monitoring
functionality. The recent rise of complex cloud applications has made
performance monitoring a major issue. We show that many performance indicators
can be inferred from messages exchanged among application components. By
analyzing these messages, we argue that the overhead of performance monitoring
could be effectively moved from the end hosts into the SDN middleware of the
cloud infrastructure which enables more flexible placement of logging
functionality. This paper explores several approaches for supporting
application monitoring through SDN. In particular, we combine selective
forwarding in SDN to enable message filtering and reformatting, and propose a
customized port sniffing technique. We describe the implementation of the
approach within the standard SDN software, namely OVS. We further provide a
comprehensive performance evaluation to analyze advantages and disadvantages of
our approach, and highlight the trade-offs
PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes
In soccer, passing is the most frequent interaction between players and plays
a significant role in creating scoring chances. Experts are interested in
analyzing players' passing behavior to learn passing tactics, i.e., how players
build up an attack with passing. Various approaches have been proposed to
facilitate the analysis of passing tactics. However, the dynamic changes of a
team's employed tactics over a match have not been comprehensively
investigated. To address the problem, we closely collaborate with domain
experts and characterize requirements to analyze the dynamic changes of a
team's passing tactics. To characterize the passing tactic employed for each
attack, we propose a topic-based approach that provides a high-level
abstraction of complex passing behaviors. Based on the model, we propose a
glyph-based design to reveal the multi-variate information of passing tactics
within different phases of attacks, including player identity, spatial context,
and formation. We further design and develop PassVizor, a visual analytics
system, to support the comprehensive analysis of passing dynamics. With the
system, users can detect the changing patterns of passing tactics and examine
the detailed passing process for evaluating passing tactics. We invite experts
to conduct analysis with PassVizor and demonstrate the usability of the system
through an expert interview
A Tracking System For Baseball Game Reconstruction
The baseball game is often seen as many contests that are performed between
individuals. The duel between the pitcher and the batter, for example, is
considered the engine that drives the sport. The pitchers use a variety of
strategies to gain competitive advantage against the batter, who does his best
to figure out the ball trajectory and react in time for a hit. In this work, we
propose a system that captures the movements of the pitcher, the batter, and
the ball in a high level of detail, and discuss several ways how this
information may be processed to compute interesting statistics. We demonstrate
on a large database of videos that our methods achieve comparable results as
previous systems, while operating solely on video material. In addition,
state-of-the-art AI techniques are incorporated to augment the amount of
information that is made available for players, coaches, teams, and fans