79 research outputs found
TPA: Fast, Scalable, and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs
Given a large graph, how can we determine similarity between nodes in a fast
and accurate way? Random walk with restart (RWR) is a popular measure for this
purpose and has been exploited in numerous data mining applications including
ranking, anomaly detection, link prediction, and community detection. However,
previous methods for computing exact RWR require prohibitive storage sizes and
computational costs, and alternative methods which avoid such costs by
computing approximate RWR have limited accuracy. In this paper, we propose TPA,
a fast, scalable, and highly accurate method for computing approximate RWR on
large graphs. TPA exploits two important properties in RWR: 1) nodes close to a
seed node are likely to be revisited in following steps due to block-wise
structure of many real-world graphs, and 2) RWR scores of nodes which reside
far from the seed node are proportional to their PageRank scores. Based on
these two properties, TPA divides approximate RWR problem into two subproblems
called neighbor approximation and stranger approximation. In the neighbor
approximation, TPA estimates RWR scores of nodes close to the seed based on
scores of few early steps from the seed. In the stranger approximation, TPA
estimates RWR scores for nodes far from the seed using their PageRank. The
stranger and neighbor approximations are conducted in the preprocessing phase
and the online phase, respectively. Through extensive experiments, we show that
TPA requires up to 3.5x less time with up to 40x less memory space than other
state-of-the-art methods for the preprocessing phase. In the online phase, TPA
computes approximate RWR up to 30x faster than existing methods while
maintaining high accuracy.Comment: 12pages, 10 figure
Application of Hellison's responsibility model in South Korea: a multiple case study of "at-risk" middle school students in Physical Education
Hellisons Teaching Personal and Social Responsibility (TPSR) model was developed in the United States but has been applied in many different countries. However, its application in East Asian cultural contexts has not been sufficiently examined. The current study describes and interprets the cultural translation of this value-based instructional model in the physical education program of a South Korean middle school. A multiple case study design was used to examine the relevance and impact of TPSR through the experiences and perceptions of six purposefully selected students who had been identified as at risk of school failure. Multiple data sources indicate that a 20-lesson TPSR unit was well-received by the students and contributed to numerous positive behavior changes. The core goals and life skills associated with TPSR appeared relevant and acceptable to case study participants, however, the concept of self-direction emerged as more challenging for them to understand and enact. This may relate to differences in cultural schemas and educational norms. Such issues, as well as implications for research and practice, are discussed. The current study expands the TPSR literature by being one of the first to examine and make a case for the implementation of TPSR in an East Asian countryEl modelo de Enseñanza de la Responsabilidad Personal y Social (TPSR) de Hellison fue desarrollado en los Estados Unidos de América pero se ha aplicado en muchos otros países. Sin embargo, su aplicación en contextos culturales de Asia oriental no ha sido suficientemente examinada. El presente estudio describe a interpreta la traducción cultural de dicho modelo de instrucción basado en valores dentro de un programa de EF en un centro de enseñanza media de Corea del Sur. Se escogió un diseño de estudio de casos multiple para examinar la relevancia y el impacto del TPSR a través de las experiencias y percepciones de seis alumnos, escogidos a propósito, que habían sido identificados como ¿en riesgo de fracaso académico¿. Múltiples fuentes de datos indican que el programa TPSR de 20 lecciones fue bien recibido por los alumnos y que contribuyó a numerosos cambios positivos de su comportamiento. Los participantes consideraron relevantes los objetivos centrales y las habilidades para la vida social asociadas al TPSR; sin embargo, el concepto de auto-direccion emergió como el más difícil de entender y llevar a cabo. Esto puede deberse a las diferencias relativas a los esquemas culturales y a las normas educativas. Se dicuten aquí estas cuestiones, así como sus implicaciones para la investigación y la práctica. Este estudio, al ser el primero que examina y propone la aplicación de TPSR en un país de Asia oriental, amplia la literatura sobre dicho modelo
Learning Disentangled Representations in Signed Directed Graphs without Social Assumptions
Signed graphs are complex systems that represent trust relationships or
preferences in various domains. Learning node representations in such graphs is
crucial for many mining tasks. Although real-world signed relationships can be
influenced by multiple latent factors, most existing methods often oversimplify
the modeling of signed relationships by relying on social theories and treating
them as simplistic factors. This limits their expressiveness and their ability
to capture the diverse factors that shape these relationships. In this paper,
we propose DINES, a novel method for learning disentangled node representations
in signed directed graphs without social assumptions. We adopt a disentangled
framework that separates each embedding into distinct factors, allowing for
capturing multiple latent factors. We also explore lightweight graph
convolutions that focus solely on sign and direction, without depending on
social theories. Additionally, we propose a decoder that effectively classifies
an edge's sign by considering correlations between the factors. To further
enhance disentanglement, we jointly train a self-supervised factor
discriminator with our encoder and decoder. Throughout extensive experiments on
real-world signed directed graphs, we show that DINES effectively learns
disentangled node representations, and significantly outperforms its
competitors in the sign prediction task.Comment: 26 pages, 11 figure
Time-aware Random Walk Diffusion to Improve Dynamic Graph Learning
How can we augment a dynamic graph for improving the performance of dynamic
graph neural networks? Graph augmentation has been widely utilized to boost the
learning performance of GNN-based models. However, most existing approaches
only enhance spatial structure within an input static graph by transforming the
graph, and do not consider dynamics caused by time such as temporal locality,
i.e., recent edges are more influential than earlier ones, which remains
challenging for dynamic graph augmentation. In this work, we propose TiaRa
(Time-aware Random Walk Diffusion), a novel diffusion-based method for
augmenting a dynamic graph represented as a discrete-time sequence of graph
snapshots. For this purpose, we first design a time-aware random walk proximity
so that a surfer can walk along the time dimension as well as edges, resulting
in spatially and temporally localized scores. We then derive our diffusion
matrices based on the time-aware random walk, and show they become enhanced
adjacency matrices that both spatial and temporal localities are augmented.
Throughout extensive experiments, we demonstrate that TiaRa effectively
augments a given dynamic graph, and leads to significant improvements in
dynamic GNN models for various graph datasets and tasks.Comment: 16 page
TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions
Many real-world datasets are represented as tensors, i.e., multi-dimensional
arrays of numerical values. Storing them without compression often requires
substantial space, which grows exponentially with the order. While many tensor
compression algorithms are available, many of them rely on strong data
assumptions regarding its order, sparsity, rank, and smoothness. In this work,
we propose TENSORCODEC, a lossy compression algorithm for general tensors that
do not necessarily adhere to strong input data assumptions. TENSORCODEC
incorporates three key ideas. The first idea is Neural Tensor-Train
Decomposition (NTTD) where we integrate a recurrent neural network into
Tensor-Train Decomposition to enhance its expressive power and alleviate the
limitations imposed by the low-rank assumption. Another idea is to fold the
input tensor into a higher-order tensor to reduce the space required by NTTD.
Finally, the mode indices of the input tensor are reordered to reveal patterns
that can be exploited by NTTD for improved approximation. Our analysis and
experiments on 8 real-world datasets demonstrate that TENSORCODEC is (a)
Concise: it gives up to 7.38x more compact compression than the best competitor
with similar reconstruction error, (b) Accurate: given the same budget for
compressed size, it yields up to 3.33x more accurate reconstruction than the
best competitor, (c) Scalable: its empirical compression time is linear in the
number of tensor entries, and it reconstructs each entry in logarithmic time.
Our code and datasets are available at https://github.com/kbrother/TensorCodec.Comment: Accepted to ICDM 2023 - IEEE International Conference on Data Mining
202
NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors
Many real-world data are naturally represented as a sparse reorderable
matrix, whose rows and columns can be arbitrarily ordered (e.g., the adjacency
matrix of a bipartite graph). Storing a sparse matrix in conventional ways
requires an amount of space linear in the number of non-zeros, and lossy
compression of sparse matrices (e.g., Truncated SVD) typically requires an
amount of space linear in the number of rows and columns. In this work, we
propose NeuKron for compressing a sparse reorderable matrix into a
constant-size space. NeuKron generalizes Kronecker products using a recurrent
neural network with a constant number of parameters. NeuKron updates the
parameters so that a given matrix is approximated by the product and reorders
the rows and columns of the matrix to facilitate the approximation. The updates
take time linear in the number of non-zeros in the input matrix, and the
approximation of each entry can be retrieved in logarithmic time. We also
extend NeuKron to compress sparse reorderable tensors (e.g. multi-layer
graphs), which generalize matrices. Through experiments on ten real-world
datasets, we show that NeuKron is (a) Compact: requiring up to five orders of
magnitude less space than its best competitor with similar approximation
errors, (b) Accurate: giving up to 10x smaller approximation error than its
best competitors with similar size outputs, and (c) Scalable: successfully
compressing a matrix with over 230 million non-zero entries.Comment: Accepted to WWW 2023 - The Web Conference 202
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