31 research outputs found
Data-driven network alignment
Biological network alignment (NA) aims to find a node mapping between
species' molecular networks that uncovers similar network regions, thus
allowing for transfer of functional knowledge between the aligned nodes.
However, current NA methods do not end up aligning functionally related nodes.
A likely reason is that they assume it is topologically similar nodes that are
functionally related. However, we show that this assumption does not hold well.
So, a paradigm shift is needed with how the NA problem is approached. We
redefine NA as a data-driven framework, TARA (daTA-dRiven network Alignment),
which attempts to learn the relationship between topological relatedness and
functional relatedness without assuming that topological relatedness
corresponds to topological similarity, like traditional NA methods do. TARA
trains a classifier to predict whether two nodes from different networks are
functionally related based on their network topological patterns. We find that
TARA is able to make accurate predictions. TARA then takes each pair of nodes
that are predicted as related to be part of an alignment. Like traditional NA
methods, TARA uses this alignment for the across-species transfer of functional
knowledge. Clearly, TARA as currently implemented uses topological but not
protein sequence information for this task. We find that TARA outperforms
existing state-of-the-art NA methods that also use topological information,
WAVE and SANA, and even outperforms or complements a state-of-the-art NA method
that uses both topological and sequence information, PrimAlign. Hence, adding
sequence information to TARA, which is our future work, is likely to further
improve its performance
G-CREWE: Graph CompREssion With Embedding for Network Alignment
Network alignment is useful for multiple applications that require
increasingly large graphs to be processed. Existing research approaches this as
an optimization problem or computes the similarity based on node
representations. However, the process of aligning every pair of nodes between
relatively large networks is time-consuming and resource-intensive. In this
paper, we propose a framework, called G-CREWE (Graph CompREssion With
Embedding) to solve the network alignment problem. G-CREWE uses node embeddings
to align the networks on two levels of resolution, a fine resolution given by
the original network and a coarse resolution given by a compressed version, to
achieve an efficient and effective network alignment. The framework first
extracts node features and learns the node embedding via a Graph Convolutional
Network (GCN). Then, node embedding helps to guide the process of graph
compression and finally improve the alignment performance. As part of G-CREWE,
we also propose a new compression mechanism called MERGE (Minimum dEgRee
neiGhbors comprEssion) to reduce the size of the input networks while
preserving the consistency in their topological structure. Experiments on all
real networks show that our method is more than twice as fast as the most
competitive existing methods while maintaining high accuracy.Comment: 10 pages, accepted at the 29th ACM International Conference
onInformation and Knowledge Management (CIKM 20