13 research outputs found
Hypergraph-based optimisations for scalable graph analytics and learning
Graph-structured data has benefits of capturing inter-connectivity (topology) and hetero geneous knowledge (node/edge features) simultaneously. Hypergraphs may glean even more information reflecting complex non-pairwise relationships and additional metadata. Graph- and hypergraph-based partitioners can model workload or communication patterns of analytics and learning algorithms, enabling data-parallel scalability while preserving the solution quality. Hypergraph-based optimisations remain under-explored for graph neural networks (GNNs), which have complex access patterns compared to analytics workloads. Furthermore, special optimisations are needed when representing dynamic graph topologies and learning incrementally from streaming data. This thesis explores hypergraph-based optimisations for several scalable graph analytics and learning tasks. First, a hypergraph sampling approach is presented that supports large-scale dynamic graphs when modelling information cascades. Next, hypergraph partitioning is applied to scale approximate similarity search, by caching the computed features of replicated vertices. Moving from analytics to learning tasks, a data-parallel GNN training algorithm is developed using hypergraph-based construction and partitioning. Its communication scheme allows scalable distributed full-batch GNN training on static graphs. Sparse adja cency patterns are captured to perform non-blocking asynchronous communications for considerable speedups (10x single machine state-of-the-art baseline) in limited memory and bandwidth environments. Distributing GNNs using the hypergraph approach, compared to the graph approach, halves the running time and achieves 15% lower message volume. A new stochastic hypergraph sampling strategy further improves communication efficiency in distributed mini-batch GNN training. The final contribution is the design of streaming partitioners to handle dynamic data within a dataflow framework. This online partitioning pipeline allows complex graph or hypergraph streams to be processed asynchronously. It facilitates low latency distributed GNNs through replication and caching. Overall, the hypergraph-based optimisations in this thesis enable the development of scalable dynamic graph applications
Hypothesis Only Baselines in Natural Language Inference
We propose a hypothesis only baseline for diagnosing Natural Language
Inference (NLI). Especially when an NLI dataset assumes inference is occurring
based purely on the relationship between a context and a hypothesis, it follows
that assessing entailment relations while ignoring the provided context is a
degenerate solution. Yet, through experiments on ten distinct NLI datasets, we
find that this approach, which we refer to as a hypothesis-only model, is able
to significantly outperform a majority class baseline across a number of NLI
datasets. Our analysis suggests that statistical irregularities may allow a
model to perform NLI in some datasets beyond what should be achievable without
access to the context.Comment: Accepted at *SEM 2018 as long paper. 12 page
Scalable Graph Convolutional Network Training on Distributed-Memory Systems
Graph Convolutional Networks (GCNs) are extensively utilized for deep
learning on graphs. The large data sizes of graphs and their vertex features
make scalable training algorithms and distributed memory systems necessary.
Since the convolution operation on graphs induces irregular memory access
patterns, designing a memory- and communication-efficient parallel algorithm
for GCN training poses unique challenges. We propose a highly parallel training
algorithm that scales to large processor counts. In our solution, the large
adjacency and vertex-feature matrices are partitioned among processors. We
exploit the vertex-partitioning of the graph to use non-blocking point-to-point
communication operations between processors for better scalability. To further
minimize the parallelization overheads, we introduce a sparse matrix
partitioning scheme based on a hypergraph partitioning model for full-batch
training. We also propose a novel stochastic hypergraph model to encode the
expected communication volume in mini-batch training. We show the merits of the
hypergraph model, previously unexplored for GCN training, over the standard
graph partitioning model which does not accurately encode the communication
costs. Experiments performed on real-world graph datasets demonstrate that the
proposed algorithms achieve considerable speedups over alternative solutions.
The optimizations achieved on communication costs become even more pronounced
at high scalability with many processors. The performance benefits are
preserved in deeper GCNs having more layers as well as on billion-scale graphs.Comment: To appear in PVLDB'2
Variational recurrent sequence-to-sequence retrieval for stepwise illustration
We address and formalise the task of sequence-to-sequence (seq2seq) cross-modal retrieval. Given a sequence of text passages as query, the goal is to retrieve a sequence of images that best describes and aligns with the query. This new task extends the traditional cross-modal retrieval, where each image-text pair is treated independently ignoring broader context. We propose a novel variational recurrent seq2seq (VRSS) retrieval model for this seq2seq task. Unlike most cross-modal methods, we generate an image vector corresponding to the latent topic obtained from combining the text semantics and context. This synthetic image embedding point associated with every text embedding point can then be employed for either image generation or image retrieval as desired. We evaluate the model for the application of stepwise illustration of recipes, where a sequence of relevant images are retrieved to best match the steps described in the text. To this end, we build and release a new Stepwise Recipe dataset for research purposes, containing 10K recipes (sequences of image-text pairs) having a total of 67K image-text pairs. To our knowledge, it is the first publicly available dataset to offer rich semantic descriptions in a focused category such as food or recipes. Our model is shown to outperform several competitive and relevant baselines in the experiments. We also provide qualitative analysis of how semantically meaningful the results produced by our model are through human evaluation and comparison with relevant existing methods
Characterizing the impact of geometric properties of word embeddings on task performance
Analysis of word embedding properties to inform their use in downstream NLP
tasks has largely been studied by assessing nearest neighbors. However,
geometric properties of the continuous feature space contribute directly to the
use of embedding features in downstream models, and are largely unexplored. We
consider four properties of word embedding geometry, namely: position relative
to the origin, distribution of features in the vector space, global pairwise
distances, and local pairwise distances. We define a sequence of
transformations to generate new embeddings that expose subsets of these
properties to downstream models and evaluate change in task performance to
understand the contribution of each property to NLP models. We transform
publicly available pretrained embeddings from three popular toolkits (word2vec,
GloVe, and FastText) and evaluate on a variety of intrinsic tasks, which model
linguistic information in the vector space, and extrinsic tasks, which use
vectors as input to machine learning models. We find that intrinsic evaluations
are highly sensitive to absolute position, while extrinsic tasks rely primarily
on local similarity. Our findings suggest that future embedding models and
post-processing techniques should focus primarily on similarity to nearby
points in vector space.Comment: Appearing in the Third Workshop on Evaluating Vector Space
Representations for NLP (RepEval 2019). 7 pages + reference
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
We present a large-scale collection of diverse natural language inference
(NLI) datasets that help provide insight into how well a sentence
representation captures distinct types of reasoning. The collection results
from recasting 13 existing datasets from 7 semantic phenomena into a common NLI
structure, resulting in over half a million labeled context-hypothesis pairs in
total. We refer to our collection as the DNC: Diverse Natural Language
Inference Collection. The DNC is available online at https://www.decomp.net,
and will grow over time as additional resources are recast and added from novel
sources.Comment: To be presented at EMNLP 2018. 15 page
Scalable graph convolutional network training on distributed-memory systems
Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessary. Since the convolution operation on graphs induces irregular memory access patterns, designing a memory- and communication-efficient parallel algorithm for GCN training poses unique challenges. We propose a highly parallel training algorithm that scales to large processor counts. In our solution, the large adjacency and vertex-feature matrices are partitioned among processors. We exploit the vertex-partitioning of the graph to use non-blocking point-to-point communication operations between processors for better scalability. To further minimize the parallelization overheads, we introduce a sparse matrix partitioning scheme based on a hypergraph partitioning model for full-batch training. We also propose a novel stochastic hypergraph model to encode the expected communication volume in mini-batch training. We show the merits of the hypergraph model, previously unexplored for GCN training, over the standard graph partitioning model which does not accurately encode the communication costs. Experiments performed on real-world graph datasets demonstrate that the proposed algorithms achieve considerable speedups over alternative solutions. The optimizations achieved on communication costs become even more pronounced at high scalability with many processors. The performance benefits are preserved in deeper GCNs having more layers as well as on billion-scale graphs
RoleSim* : scaling axiomatic role-based similarity ranking on large graphs
RoleSim and SimRank are among the popular graph-theoretic similarity measures with many applications in, e.g., web search, collaborative filtering, and sociometry. While RoleSim addresses the automorphic (role) equivalence of pairwise similarity which SimRank lacks, it ignores the neighboring similarity information out of the automorphically equivalent set. Consequently, two pairs of nodes, which are not automorphically equivalent by nature, cannot be well distinguished by RoleSim if the averages of their neighboring similarities over the automorphically equivalent set are the same. To alleviate this problem: 1) We propose a novel similarity model, namely RoleSim*, which accurately evaluates pairwise role similarities in a more comprehensive manner. RoleSim* not only guarantees the automorphic equivalence that SimRank lacks, but also takes into account the neighboring similarity information outside the automorphically equivalent sets that are overlooked by RoleSim. 2) We prove the existence and uniqueness of the RoleSim* solution, and show its three axiomatic properties (i.e., symmetry, boundedness, and non-increasing monotonicity). 3) We provide a concise bound for iteratively computing RoleSim* formula, and estimate the number of iterations required to attain a desired accuracy. 4) We induce a distance metric based on RoleSim* similarity, and show that the RoleSim* metric fulfills the triangular inequality, which implies the sum-transitivity of its similarity scores. 5) We present a threshold-based RoleSim* model that reduces the computational time further with provable accuracy guarantee. 6) We propose a single-source RoleSim* model, which scales well for sizable graphs. 7) We also devise methods to scale RoleSim* based search by incorporating its triangular inequality property with partitioning techniques. Our experimental results on real datasets demonstrate that RoleSim* achieves higher accuracy than its competitors while scaling well on sizable graphs with billions of edges
Temporal cascade model for analyzing spread in evolving networks
Current approaches for modeling propagation in networks (e.g., of diseases, computer viruses, rumors) cannot adequately capture temporal properties such as order/duration of evolving connections or dynamic likelihoods of propagation along connections. Temporal models on evolving networks are crucial in applications that need to analyze dynamic spread. For example, a disease spreading virus has varying transmissibility based on interactions between individuals occurring with different frequency, proximity, and venue population density. Similarly, propagation of information having a limited active period, such as rumors, depends on the temporal dynamics of social interactions. To capture such behaviors, we first develop the Temporal Independent Cascade (T-IC) model with a spread function that efficiently utilizes a hypergraph-based sampling strategy and dynamic propagation probabilities. We prove this function to be submodular, with guarantees of approximation quality. This enables scalable analysis on highly granular temporal networks where other models struggle, such as when the spread across connections exhibits arbitrary temporally evolving patterns. We then introduce the notion of ‘reverse spread’ using the proposed T-IC processes, and develop novel solutions to identify both sentinel/detector nodes and highly susceptible nodes. Extensive analysis on real-world datasets shows that the proposed approach significantly outperforms the alternatives in modeling both if and how spread occurs, by considering evolving network topology alongside granular contact/interaction information. Our approach has numerous applications, such as virus/rumor/influence tracking. Utilizing T-IC, we explore vital challenges of monitoring the impact of various intervention strategies over real spatio-temporal contact networks where we show our approach to be highly effective