Fetching the paper…
Reading the bibliography…
The topological (or graph) structures of real-world networks are known to be predictive of multiple dynamic properties of the networks.
The network structure of social capital
R. S. Burt · 2000
Earlier work this paper cites.
On graph kernels: Hardness results and efficient alternatives
T. Gärtner, P. Flach, and S. Wrobel · 2003
Earlier work this paper cites.
Heat kernels, manifolds and graph embedding
X. Bai and E. R. Hancock · 2004
Earlier work this paper cites.
Kernels for graphs
H. Kashima, K. Tsuda, and A. Inokuchi · 2004
Earlier work this paper cites.
Shortest-path kernels on graphs
K. M. Borgwardt and H.-P. Kriegel · 2005
Earlier work this paper cites.
Laplacians and the cheeger inequality for directed graphs
F. Chung · 2005
Earlier work this paper cites.
Team assembly mechanisms determine collaboration network structure and team performance
R. Guimera, B. Uzzi, J. Spiro, and L. A. N. Amaral · 2005
Earlier work this paper cites.
Group formation in large social networks: membership, growth, and evolution
L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan · 2006
Earlier work this paper cites.
Empirical analysis of an evolving social network
G. Kossinets and D. J. Watts · 2006
Earlier work this paper cites.
Fast unfolding of communities in large networks
V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre · 2008
Earlier work this paper cites.
Mining social networks using heat diffusion processes for marketing candidates selection
H. Ma, H. Yang, M. R. Lyu, and I. King · 2008
Earlier work this paper cites.
A Bibliometric and Network Analysis of the field of Computational Linguistics
B. G. P. M. Dragomir R. Radev, Mark Thomas Joseph · 2009
Earlier work this paper cites.
Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
A. Mislove · 2009
Earlier work this paper cites.
Graphsig: A scalable approach to mining significant subgraphs in large graph databases
S. Ranu and A. K. Singh · 2009
Earlier work this paper cites.
gboost: a mathematical programming approach to graph classification and regression
H. Saigo, S. Nowozin, T. Kadowaki, T. Kudo, and K. Tsuda · 2009
Earlier work this paper cites.
Efficient graphlet kernels for large graph comparison
N. Shervashidze, T. Petri, K. Mehlhorn, K. M. Borgwardt, and S. Vishwanathan · 2009
Cited alongside, same era.
A concise and provably informative multi-scale signature based on heat diffusion
J. Sun, M. Ovsjanikov, and L. Guibas · 2009
Cited alongside, same era.
On the evolution of user interaction in facebook
B. Viswanath, A. Mislove, M. Cha, and K. P. Gummadi · 2009
Cited alongside, same era.
Networks, crowds, and markets: Reasoning about a highly connected world
D. Easley and J. Kleinberg · 2010
Cited alongside, same era.
Predicting the popularity of online content
G. Szabo and B. A. Huberman · 2010
Cited alongside, same era.
Modeling information diffusion in implicit networks
J. Yang and J. Leskovec · 2010
Cited alongside, same era.
Can cascades be predicted?
J. Cheng, L. Adamic, P. A. Dow, J. M. Kleinberg, and J. Leskovec · 2014
Later among the works it cites.
Deepwalk: Online learning of social representations
B. Perozzi, R. Al-Rfou, and S. Skiena · 2014
Later among the works it cites.
A quantum jensen–shannon graph kernel for unattributed graphs
L. Bai, L. Rossi, A. Torsello, and E. R. Hancock · 2015
Later among the works it cites.
Does team competition increase pro-social lending? evidence from online microfinance
R. Chen, Y. Chen, Y. Liu, and Q. Mei · 2015
Later among the works it cites.
Heat-passing framework for robust interpretation of data in networks
Y. Fang, M. Sun, and K. Ramani · 2015
Later among the works it cites.
3d deep shape descriptor
Y. Fang, J. Xie, G. Dai, M. Wang, F. Zhu, T. Xu, and E. Wong · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Weisfeiler-lehman graph kernels
N. Shervashidze, P. Schweitzer, E. J. Van Leeuwen, K. Mehlhorn, and K. M. Borgwardt · 2011
Cited alongside, same era.
Pathsim: Meta path-based top-k similarity search in heterogeneous information networks
Y. Sun, J. Han, X. Yan, P. S. Yu, and T. Wu · 2011
Cited alongside, same era.
Prediction of retweet cascade size over time
A. Kupavskii, L. Ostroumova, A. Umnov, S. Usachev, P. Serdyukov, G. Gusev, and A. Kustarev · 2012
Cited alongside, same era.
What’s in a hashtag?: content based prediction of the spread of ideas in microblogging communities
O. Tsur and A. Rappoport · 2012
Cited alongside, same era.
Structural diversity in social contagion
J. Ugander, L. Backstrom, C. Marlow, and J. Kleinberg · 2012
Cited alongside, same era.
We know what@ you# tag: does the dual role affect hashtag adoption?
L. Yang, T. Sun, M. Zhang, and Q. Mei · 2012
Cited alongside, same era.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
Later among the works it cites.
Pte: Predictive text embedding through large-scale heterogeneous text networks
J. Tang, M. Qu, and Q. Mei · 2015
Later among the works it cites.
Deepshape: Deep learned shape descriptor for 3d shape matching and retrieval
J. Xie, Y. Fang, F. Zhu, and E. Wong · 2015
Later among the works it cites.
Deep graph kernels
P. Yanardag and S. Vishwanathan · 2015
Later among the works it cites.
A structural smoothing framework for robust graph comparison
P. Yanardag and S. Vishwanathan · 2015
Later among the works it cites.
node2vec: Scalable feature learning for networks
A. Grover and J. Leskovec · 2016
Closest in time.
subgraph2vec: Learning distributed representations of rooted sub-graphs from large graphs
A. Narayanan, M. Chandramohan, L. Chen, Y. Liu, and S. Saminathan · 2016
Closest in time.
Learning convolutional neural networks for graphs
M. Niepert, M. Ahmed, and K. Kutzkov · 2016
Closest in time.