31 research outputs found
Simultaneous Optimization of Both Node and Edge Conservation in Network Alignment via WAVE
Network alignment can be used to transfer functional knowledge between
conserved regions of different networks. Typically, existing methods use a node
cost function (NCF) to compute similarity between nodes in different networks
and an alignment strategy (AS) to find high-scoring alignments with respect to
the total NCF over all aligned nodes (or node conservation). But, they then
evaluate quality of their alignments via some other measure that is different
than the node conservation measure used to guide the alignment construction
process. Typically, one measures the amount of conserved edges, but only after
alignments are produced. Hence, a recent attempt aimed to directly maximize the
amount of conserved edges while constructing alignments, which improved
alignment accuracy. Here, we aim to directly maximize both node and edge
conservation during alignment construction to further improve alignment
accuracy. For this, we design a novel measure of edge conservation that (unlike
existing measures that treat each conserved edge the same) weighs each
conserved edge so that edges with highly NCF-similar end nodes are favored. As
a result, we introduce a novel AS, Weighted Alignment VotEr (WAVE), which can
optimize any measures of node and edge conservation, and which can be used with
any NCF or combination of multiple NCFs. Using WAVE on top of established
state-of-the-art NCFs leads to superior alignments compared to the existing
methods that optimize only node conservation or only edge conservation or that
treat each conserved edge the same. And while we evaluate WAVE in the
computational biology domain, it is easily applicable in any domain.Comment: 12 pages, 4 figure
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Stronger generalization bounds for deep nets via a compression approach
Deep nets generalize well despite having more parameters than the number of training samples. Recent works try to give an explanation using PAC-Bayes and Margin-based analyses, but do not as yet result in sample complexity bounds better than naive parameter counting. The current paper shows generalization bounds that are orders of magnitude better in practice. These rely upon new succinct reparametrizations of the trained net - a compression that is explicit and efficient. These yield generalization bounds via a simple compression-based framework introduced here. Our results also provide some theoretical justification for widespread empirical success in compressing deep nets. Analysis of correctness of our compression relies upon some newly identified "noise stability"properties of trained deep nets, which are also experimentally verified. The study of these properties and resulting generalization bounds are also extended to convolutional nets, which had eluded earlier attempts on proving generalization
On the Complexity of Inner Product Similarity Join
A number of tasks in classification, information retrieval, recommendation
systems, and record linkage reduce to the core problem of inner product
similarity join (IPS join): identifying pairs of vectors in a collection that
have a sufficiently large inner product. IPS join is well understood when
vectors are normalized and some approximation of inner products is allowed.
However, the general case where vectors may have any length appears much more
challenging. Recently, new upper bounds based on asymmetric locality-sensitive
hashing (ALSH) and asymmetric embeddings have emerged, but little has been
known on the lower bound side. In this paper we initiate a systematic study of
inner product similarity join, showing new lower and upper bounds. Our main
results are:
* Approximation hardness of IPS join in subquadratic time, assuming the
strong exponential time hypothesis.
* New upper and lower bounds for (A)LSH-based algorithms. In particular, we
show that asymmetry can be avoided by relaxing the LSH definition to only
consider the collision probability of distinct elements.
* A new indexing method for IPS based on linear sketches, implying that our
hardness results are not far from being tight.
Our technical contributions include new asymmetric embeddings that may be of
independent interest. At the conceptual level we strive to provide greater
clarity, for example by distinguishing among signed and unsigned variants of
IPS join and shedding new light on the effect of asymmetry.Comment: in Proc. 35th ACM Symposium on Principles of Database Systems, 201
Triad-based comparison and signatures of directed networks
We introduce two methods for comparing directed networks based on triad counts, called TriadEuclid and TriadEMD. TriadEuclid clusters the Euclidean distance between triad counts, whereas TriadEMD is an adaptation of NetEMD for directed networks. We apply both methods to cluster synthetic networks, a set of web networks including google, twitter, peer-to-peer, amazon, slashdot and citation networks, as well as world trade networks from 1962-2000. Furthermore, we find signature triads and signature orbits for each type of networks in our data, which show the main triad and orbit contributions of the networks when comparing them to the other networks in the respective data set
Continuously Adaptive Similarity Search
Similarity search is the basis for many data analytics techniques, including k-nearest neighbor classification and outlier detection. Similarity search over large data sets relies on i) a distance metric learned from input examples and ii) an index to speed up search based on the learned distance metric. In interactive systems, input to guide the learning of the distance metric may be provided over time. As this new input changes the learned distance metric, a naive approach would adopt the costly process of re-indexing all items after each metric change. In this paper, we propose the first solution, called OASIS, to instantaneously adapt the index to conform to a changing distance metric without this prohibitive re-indexing process. To achieve this, we prove that locality-sensitive hashing (LSH) provides an invariance property, meaning that an LSH index built on the original distance metric is equally effective at supporting similarity search using an updated distance metric as long as the transform matrix learned for the new distance metric satisfies certain properties. This observation allows OASIS to avoid recomputing the index from scratch in most cases. Further, for the rare cases when an adaption of the LSH index is shown to be necessary, we design an efficient incremental LSH update strategy that re-hashes only a small subset of the items in the index. In addition, we develop an efficient distance metric learning strategy that incrementally learns the new metric as inputs are received. Our experimental study using real world public datasets confirms the effectiveness of OASIS at improving the accuracy of various similarity search-based data analytics tasks by instantaneously adapting the distance metric and its associated index in tandem, while achieving an up to 3 orders of magnitude speedup over the state-of-art techniques