Know your neighbours: Machine Learning on Graphs

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1 Know your neighbours: Machine Learning on Graphs Andrew Docherty Senior Research Engineer

2 2 Graphs are Everywhere Online Social Networks Transportation Networks Power Networks

3 Networks are Everywhere Topic models Financial transactions Terrorist Networks 3 Valdis Krebs,

4 4 What is a graph/network? node / vertex graph / network

5 5 What is a graph/network? node / vertex edge / link graph / network

6 6 What is a graph/network? Node types Heterogeneous graph / network Edge types

7 7 <latexit sha1_base64="p2ztnnfntkipyhj1fz96crcv/ug=">aaacfxicbzdlssnafiyn9vbrrerszwarxwhirpacqsgnywrgfprstqbtduhkemzoxbl6fg58ftcuvnwk7nwbp20w2npg4op/z+hm+ynyca2o823l5uyxfpfyy4wv1bx1jelm1p2oekwzrymrqvpanbncmg84cfalfsnhifg16f+n/oo9u5ph8hygmwuepct5h1mcrmowj0ht4kvsa3ua1e9bipo4+xfytu1d3cjdnmhsd4fnysmxnxhhwxazkkgsks3il9+oabiycvqqreuue0mjjqo4fwxy8bpnykl7zkvdocqh0410fnyq7xmljturmk8chqu/j1isaj0ia9mzeujpaw8k/ufve+icnviu4wsypjnfnurgipaoi9zmileqawoekm7+immpkelbjfkwibjtj8+cd2yf2+7nsalcztliox20iw6qi05rgv2jcviqry/ogb2in+vjerhery9ja87kzrbrn7i+fwbnx5x7</latexit> <latexit sha1_base64="d8xmnb8j/4th+o9jv8ogl9cpwge=">aaacbxicbvdjsgnbeo2jw4xb1kmijuh0nmyi4ajcwivhcmyemih2dcpjk56f7hoxdhpy4q948adi1x/w5t/ywq6a+kdg8v4vvfx8waqnjvnt5ebmfxax8sufldw19y3i5tatjhlfocojgam6zzrieuivbuqoxwpy4euo+f3lov+7b6vffn7giizmwlqh6ajo0eit4i5rab2ghsidpl56hykag3nq2zb1sqxvldm2mwkdje6elmgelvbxy2thpakgrc6z1g3xibgzmowcs8gkxqihzrzputawngqb6gy6eioj+0zp006ktivir+rvizqfwg8c33qgdht62huk/3mnbdunzvsecyiq8vgitiiprnsycw0lbrzlwbdgltc3ut5jine0yrvmco70y7okemsf2e71calcnqsrjztkjxwsl5yqmrkifvilndysz/jk3qwn68v6tz7grtlrmrnn/sd6/afew5de</latexit> What is a graph/network? node attributes Attributed graph / network edge attributes

8 8 Machine learning and the table Columns are attributes of the entity Rows are instances of an entity First Last Gender Age Income Homer Simpson M Marge Simpson F 36 0 Bart Simpson M 10 0 Lisa Simpson F 8 0 Carl Carlson M 43 Lenny Leonard M Ned Flanders M

9 9 <latexit sha1_base64="y6lmc7chuulx4krbzxquxvtmr9c=">aaacf3icbva9swnben3zm8avu0ubxsdejt6johybg8singkkiext5pilex/szqnhym+w8a/ywkjyaue/czncoykpbt6+n8popd+rqqpjffsli0vlk6uftel6xubwtr2ze6vjvhhwecxj1fczbiki8fcgheaigiw+hlo/ubz79ttqwstrdq4taiesf4lacizg6tjhlyqhzjcphucom6atcyf5qfiebuwpgqddvie29lhhljkvzwi6t9ycleiowsf+anvjnoyqizdm66brjnjomelbjyykrvrdwvia9abpamrc0o1sctiihhqls4nymyqqtttfexkltr6gvukmgfb1rdcw//oakqbn7uxesyoq8elhqsopxnsceu0kbrzl0bdgltc7ut5nine0wrznco7syfpeo6lcvnzr01k1mqdripvkgjsjs85ilvyrgveij4/kmbysn+vjerhery9p64kvz+yrp7a+fwdzakeo</latexit> Machine learning and the table Features Target First Last Gender Age Income Homer Simpson M Marge Simpson F 36 0 Predict Target Using Features Bart Simpson M 10 0 Lisa Simpson F 8 0 Carl Carlson M 43? Lenny Leonard M Ned Flanders M

10 10 <latexit sha1_base64="hge8gqzoag2bwpzjckfrnk0nltq=">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</latexit> Machine learning and the table Independence assumption: Every instance of an entity is independent of other instances: In reality instances are often connected and not independent Sex: M Income:? Age: 43 Edge type represents a relation between instances Sex: M Income: Age: 43 Sex: M Income: Age: 45 Colleague Neighbour Family

11 11 Machine Learning on Graphs Classical approach: For each node insert structural properties as additional features in the table. Typical graph features: Node degree (number of neighbours); Centrality measures (betweenness, closeness); Subgraph matches (triangle count); Many others! 2 3 1

12 12 Example: Identifying Spammers Content based spam identification: Message 50% off Win Analyse content Instant Cash Machine learning classifier Spam Not-Spam Network action based spammer identification at Tagged.com: 1 Fakhraei et al. Collective Spammer Detection in Evolving Multi-Relational Social Networks KDD 15

13 13 Example: Identifying Spammers Temporal Features Could classify 70% of spammers with 90% accuracy. 1 Fakhraei et al. Collective Spammer Detection in Evolving Multi-Relational Social Networks KDD 15

14 14 Deep learning on Graphs They are currently the state-of-the-art algorithms for many graphbased machine learning tasks.

15 15 Example: Movie recommendations Users rate movies (with a point scale of stars) User Rating 1-5 Movie Movielens dataset: Movielens 1M 6,040 Users 3,900 Movies 1,000,209 Ratings Movie features Year Genre Critic ratings User features Gender Age Occupation Harper et al. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems

16 16 Baseline: Collaborative Filtering Users that rate other movies similarly to you are used to predict the rating for a movie you haven t rated.

17 17 Baseline: Collaborative Filtering Users that rate other movies similarly to you are used to predict the rating for a movie you haven t rated.

18 18 Example: Movie recommendations Collaborative filtering Users that rate other movies similarly to you are used to predict the rating for a movie you haven t rated. Content-based methods These methods aim to use movie features (genre, year, critic rating etc) and user profiles (preferences, demographics) to predict which movies will be favoured by particular users. Social-based methods People like what their close friends like. Social networks can be used to improve recommendations. Hybrid methods: These aim to combined multiple previous methods using feature combination, weighted scoring, and other techniques. Konstan and Riedl. Recommender systems: from algorithms to user experience UMUAI 2012, dx.doi.org/ /s x

19 19 <latexit sha1_base64="cdbvlf1xu2cpegiklulsp13g4zo=">aaab6xicbvbns8naej34wetx1aoxxsj4koki6q3gxwnfywttkjvtpf272ytdjvbdf4ixdype/ufe/ddu2xy09cha470zzuafqedauo63s7s8srq2xtoob25t7+xw9vbvdziphj5lrkjaiduouetfccowlsqkcsiwgq6vjn7zezxmibwzoxsdmpyljzijxkq3t92hbqxq1twpyclxclkfao1u5avts1gwozrmuk3bnpuaikfkcczwxo5kglpkhrspbusljveh+ftumtm2so9eibildzmqvydygms9ikpbgvmz0pperpzpa2cmughyltpmogszrvemieni5g/s4wqzesnlkfpc3krygcrkje2nbepw5l9ejp5p7blm3zxv6/uijricwhgcgafnuidraiapdprwdk/w5gjnxxl3pmats04xcwb/4hz+aniqja0=</latexit> <latexit sha1_base64="7gvkpuuddnhfjc+fkbocycmlxkm=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8flx4rgltoq9lsn+3szsbstoqa+ho8efdx6j/y5r9x2+agrq8ghu/nmdmvtkuw6lrftmlldw19o7xz2dre2d2r7h88mcttjpsskyluh9rwkrt3uadk7vrzgoest8lr9drvpxjtrkluczzyikydjslbkfrp7qk36lvrbt2dgswtrya1kndsvb+6/yrlmvfijdwm47kpbjnvkjjkk0o3mzylbeqhvgopoje3qt47dujorninuajtksqz9fdetmnjxnfoo2okq7potcx/ve6g0wwqc5vmybwbl4oystah079jx2joui4touwleythq6opq5toxybglb68tpyz+lxduz2vnrpfgmu4gmm4bq8uoae30aqfgazggv7hzzhoi/pufmxbs04xcwh/4hz+anotja4=</latexit> <latexit sha1_base64="u9emzzho7buvbqhprrx9jegz6wm=">aaab7xicbvbns8naej3ur1q/qh69lbbbg5rebpvw8okxgrgfnptndtou3wzc7kqoot/ciwcvr/4fb/4bt20o2vpg4pheddpzwlqkg6777zrwvtfwn8qbla3tnd296v7bg0kyzbjpepnodkgnl0jxhwvk3k41p3eoessc3uz91hpxritqhscpd2i6ucisjkkvwrqxp56njr1qza27m5bl4hwkbgwavepxt5+wloykmatgddw3xscnggwtfflpzoanli3oghcsvttmjshn507iivx6jeq0lyvkpv6eyglszdgobwdmcwgwvan4n9fjmlokcqhsdlli80vrjgkmzpo76qvngcqxjzrpyw8lbeg1zwgtqtgqvmwxl4l/xr+ue3cxtuajskmmr3amp+dbjttgfprga4mrpmmrvdmp8+k8ox/z1pjtzbzchzifp7wbj1w=</latexit> Recommender systems with Graph ML Neural networks are now state-of-the-art for Graph ML How might we build a neural-network based recommender? User j We want to predict this rating. Rating Movie k Fully-connected neural network

20 20 <latexit sha1_base64="vt3a/twh4egbkeiuc6js1ydbvjo=">aaab73icbvbns8naej34wetx1aoxxsj4kjkion4kxjxwmlbshrdzbtptdzdhdypu0f/hxyokv/+on/+n2zyhbx0w8hhvhpl5ucqznq777swtr6yurzc2yptb2zu7lb39e51kilcfjdxrrqhrypmkvmgg01aqkbyrp81oed3xm49uazbioznkasbwt7kyewys9pau5ontgq7gyaxq1twp0clxclkfao2w8txpjiqtvbrcsdztz01nkgnlgof0xo5kmqaydhgpti2vwfad5nodx+jykl0uj8qwngiq/p7isdb6jclbkbdp63lviv7nttmtxwy5k2lmqcszrxhgkunq5hvuzyosw0ewykkyvrwrplaygjtr2ybgzb+8spyz2lxnuz2v1utfgiu4hcm4aq8uoa430aafcah4hld4c5tz4rw7h7pwjaeyoya/cd5/aeygkeq=</latexit> <latexit sha1_base64="wuadcoazdjf2hov2auxfjnqmtw4=">aaab73icbvbns8naej34wetx1aoxxsj4kjkion4kxjxwmlbshrdzbtolu5uwuxfq6k/w4khfq3/hm//gbzudtj4yelw3w8y8konmg9f9dpawv1bx1isb1c2t7z3d2t7+vu5zrahpup6qtoq15uxs3zddasdtfiui03auxe/89invmqxyzowyggg8kcxmbbsrptyfrxiqw2qc1upuw50clrkvjhuo0qprx71+snjbpseca9313mwebvageu7h1v6uayzjgge0a6neguqgmb48rsdw6am4vbakqvp190sbhdyjedlogc1qz3st8t+vm5v4miiyzhjdjzktinootiom36m+u5qypriee8xsrygmscle2iyqngrv/uvf4p81rhre7xm92sztqmahhmejehabtbibfvhaqmazvmkbo5wx5935mluuoexmafyb8/kdt6yqrg==</latexit> <latexit sha1_base64="vv4vqzhtdz+vxaal8jbzgaoaxwq=">aaab7xicbvbns8naej3ur1q/qh69lbbbg5rebpvw8okxgrgfnptndtmu3wzc7ksoot/ciwcvr/4fb/4bt20o2vpg4pheddpzwlqkg6777zrwvtfwn8qbla3tnd296v7bg0kyzbjpepnodkgnl0jxhwvk3k41p3eoessc3uz91ipxritqhscpd2i6ucisjkkvwk+9fhtmtxrvmlt3zydlxctidqo0e9wvbj9hwcwvmkmn6xhuikfonqom+atszqxpkrvrae9yqmjmtzdpzp2qe6v0szrowwrjtp09kdpymhec2s6y4taselpxp6+tyxqv5eklgxlf5ouitbjmypr30heam5rjsyjtwt5k2jbqytamvleheisvlxp/vh5d9+4uao1gkuyzjuaytsgds2jalttbbwyjeizxehns58v5dz7mrswnmdmep3a+fwbyay8r</latexit> <latexit sha1_base64="0eqrwf6ipgksd+t4xvxicwnsvyu=">aaab7xicbvbns8naej34wetx1aoxxsj4kjkion4kxjxwmlbqhrlzbtq1m03ynqg19ed48adi1f/jzx/jts1bwx8mpn6bywzemeph0hw/naxlldw19djgexnre2e3srd/b5jmm+6zrca6fvldpvdcr4gst1lnarxk3gyh1xo/+ci1eym6w1hkg5j2lygeo2il5lm3fzj1xt1k1a25u5bf4hwkcgua3cpxp5ewloykmatgtd03xscnggwtffzuzianla1pn7ctvttmjsin547jsvv6jeq0lyvkqv6eyglszcgobwdmcwdmvyn4n9fomlomcqhsdllis0vrjgkmzpi76qnngcqrjzrpyw8lbea1zwgtktsqvpmxf4l/vruqebfn1xq9skmeh3aej+dbbdthbhrga4mhpmmrvdmp8+k8ox+z1iwnmdmap3a+fwbwe48q</latexit> <latexit sha1_base64="8d2ytthki0f5v+yac4za3nqypjs=">aaab6nicbvbnswmxej3ur1q/qh69bivgqeykon4kxjxwcg2hxuo2zbahsxzjskjz+he8efdx6i/y5r8x2+5bwx8mpn6bywzelapurod9o8ra+sbmvnw7tro7t39qpzx6nemmkqtoihldjyhhgiswwg4f66aaerkj1okmt4xfewla8eq92gnkqklgiseceltifzpjqb3hnb058crxs9kaeu1b/as/tggmmbjuegn6vpfamcfaciryrnbpdesjnzar6zmqigqmzoe3zvczu4y4trqrzffc/t2re2nmveauuxi7nsteif7n9tibx4c5v2lmmaklrxemse1w8tgecs2ofvnhcnxc3yrpmghcryun5klwl19ejcff86bp3182wq0yjsqcwcmcgw9x0ii7aemafmbwdk/whir6qe/oy9faqexmmfwb+vwbnmyojq==</latexit> <latexit sha1_base64="8d2ytthki0f5v+yac4za3nqypjs=">aaab6nicbvbnswmxej3ur1q/qh69bivgqeykon4kxjxwcg2hxuo2zbahsxzjskjz+he8efdx6i/y5r8x2+5bwx8mpn6bywzelapurod9o8ra+sbmvnw7tro7t39qpzx6nemmkqtoihldjyhhgiswwg4f66aaerkj1okmt4xfewla8eq92gnkqklgiseceltifzpjqb3hnb058crxs9kaeu1b/as/tggmmbjuegn6vpfamcfaciryrnbpdesjnzar6zmqigqmzoe3zvczu4y4trqrzffc/t2re2nmveauuxi7nsteif7n9tibx4c5v2lmmaklrxemse1w8tgecs2ofvnhcnxc3yrpmghcryun5klwl19ejcff86bp3182wq0yjsqcwcmcgw9x0ii7aemafmbwdk/whir6qe/oy9faqexmmfwb+vwbnmyojq==</latexit> <latexit sha1_base64="cdbvlf1xu2cpegiklulsp13g4zo=">aaab6xicbvbns8naej34wetx1aoxxsj4koki6q3gxwnfywttkjvtpf272ytdjvbdf4ixdype/ufe/ddu2xy09cha470zzuafqedauo63s7s8srq2xtoob25t7+xw9vbvdziphj5lrkjaiduouetfccowlsqkcsiwgq6vjn7zezxmibwzoxsdmpyljzijxkq3t92hbqxq1twpyclxclkfao1u5avts1gwozrmuk3bnpuaikfkcczwxo5kglpkhrspbusljveh+ftumtm2so9eibildzmqvydygms9ikpbgvmz0pperpzpa2cmughyltpmogszrvemieni5g/s4wqzesnlkfpc3krygcrkje2nbepw5l9ejp5p7blm3zxv6/uijricwhgcgafnuidraiapdprwdk/w5gjnxxl3pmats04xcwb/4hz+aniqja0=</latexit> <latexit sha1_base64="7gvkpuuddnhfjc+fkbocycmlxkm=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8flx4rgltoq9lsn+3szsbstoqa+ho8efdx6j/y5r9x2+agrq8ghu/nmdmvtkuw6lrftmlldw19o7xz2dre2d2r7h88mcttjpsskyluh9rwkrt3uadk7vrzgoest8lr9drvpxjtrkluczzyikydjslbkfrp7qk36lvrbt2dgswtrya1kndsvb+6/yrlmvfijdwm47kpbjnvkjjkk0o3mzylbeqhvgopoje3qt47dujorninuajtksqz9fdetmnjxnfoo2okq7potcx/ve6g0wwqc5vmybwbl4oystah079jx2joui4touwleythq6opq5toxybglb68tpyz+lxduz2vnrpfgmu4gmm4bq8uoae30aqfgazggv7hzzhoi/pufmxbs04xcwh/4hz+anotja4=</latexit> <latexit sha1_base64="u9emzzho7buvbqhprrx9jegz6wm=">aaab7xicbvbns8naej3ur1q/qh69lbbbg5rebpvw8okxgrgfnptndtou3wzc7kqoot/ciwcvr/4fb/4bt20o2vpg4pheddpzwlqkg6777zrwvtfwn8qbla3tnd296v7bg0kyzbjpepnodkgnl0jxhwvk3k41p3eoessc3uz91hpxritqhscpd2i6ucisjkkvwrqxp56njr1qza27m5bl4hwkbgwavepxt5+wloykmatgddw3xscnggwtfflpzoanli3oghcsvttmjshn507iivx6jeq0lyvkpv6eyglszdgobwdmcwgwvan4n9fjmlokcqhsdlli80vrjgkmzpo76qvngcqxjzrpyw8lbeg1zwgtqtgqvmwxl4l/xr+ue3cxtuajskmmr3amp+dbjttgfprga4mrpmmrvdmp8+k8ox/z1pjtzbzchzifp7wbj1w=</latexit> Recommender systems with Graph ML Movies rated by user j User j Average Rating Average Movie k Fully-connected neural network Users who rated movie k

21 21 <latexit sha1_base64="nfvu6g5jsmxhjutq6tx9lkbskk0=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8flx4rgltoq9lsj+3szsbsbsqs+ho8efdx6j/y5r9x2+agrq8ghu/nmdmvtaxxxnw/ndlk6tr6rnmzsrw9s7tx3t940emmgposeylqh1sj4bj9w43adqqqxqhavji6nvqtr1saj/lejfmmyjqqpokmgivdpfw8xrxm1t0zydlxclkdas1e9avbt1gwozrmuk07npuaikfkcczwuulmglpkrnsahusljveh+ezuctmxsp9eibildzmpvydygms9jkpbgvmz1ivevpzp62qmugxyltpmogtzrvemieni9g/s5wqzewnlkfpc3krykcrkje2nykpwfl9ejv5z/aru3z7xgo0ijticwtgcggcx0iabaiipdabwdk/w5gjnxxl3puatjaeyoyq/cd5/ahjbjxi=</latexit> <latexit sha1_base64="jnoyvhbdtoao4sgli/giizbpodu=">aaab6xicbvbns8naej34wetx1aoxxsj4koki6q3gxznunlbqhrlzttqlm03y3ygl9cd48adi1x/kzx/jts1bwx8mpn6bywzemaqujet+o0vlk6tr66wn8ubw9s5uzw//qsezyuizrcsqfvkngkv0dtccw6lcgoccm+hwaui3h1fpnsh7m0oxiglf8ogzaqx099s96vaqbs2dgiwsrybvkndovr46vyrlmurdbnw67bmpcxkqdgccx+vopjglbej72lzu0hh1ke9phznjq/rilchb0pcp+nsip7hwozi0nte1az3vtct/vhzmoosg5zlndeo2wxrlgpietp4mpa6qgtgyhdlf7a2edaiiznh0yjyeb/7lrekf1i5r3u1ztv4v0ijbirzbcxhwdnw4hgb4wkapz/akb45wxpx352pwuuqumwfwb87nd6syjy8=</latexit> <latexit sha1_base64="parslp3scte1/zi1xf9+jnm70s8=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8fl16eisyw2la22027dlmjuxohhp4elx5uvpqpvplv3ly5aoudgcd7m8zmc1mpdlrut1nawv1b3yhvvra2d3b3qvshjybjnom+s2si2ye1xarffrqoetvvnmah5k1wdd31w09cg5gobxynpijpqilimipwus96t71qza27m5bl4hwkbgwavepxt5+wloykmatgddw3xscnggwtfflpzoanli3oghcsvttmjshnp07iivx6jeq0lyvkpv6eyglszdgobwdmcwgwvan4n9fjmlomcqhsdlli80vrjgkmzpo36qvngcqxjzrpyw8lbeg1zwjtqdgqvmwxl4l/vr+qe3fntuajskmmr3amp+dbbttgbprga4mbpmmrvdnsexheny95a8kpzg7hd5zph56gjys=</latexit> <latexit sha1_base64="1uklseon2r96dpcjhfzod3fknam=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leqn4kxjxwnlbqhrlzttqlm03y3qgl9cd48adi1x/kzx/jts1bwx8mpn6bywzemaqujet+o6w19y3nrfj2zwd3b/+genj0qjnmmfrzihlvcalgwsx6hhubnvqhjuob7xb8m/pbt6g0t+sdmaqyxhqoecqznva6z/pev1pz6+4czjv4balbgva/+tubjcyluromqnzdz01nkfnlobm4rfqyjsllyzrerqwsxqidfh7qljxzzucirnmshszv3xm5jbwexkhtjkkz6wvvjv7ndtmtxqu5l2lmullfoigtxcrk9jczcixmiikllclubyvsrbvlxqztssf4yy+vev+ifl337i5rzwarrhlo4btowymgnoewwuadgye8wyu8ocj5cd6dj0vryslmjuepnm8fdeynbw==</latexit> <latexit sha1_base64="8d2ytthki0f5v+yac4za3nqypjs=">aaab6nicbvbnswmxej3ur1q/qh69bivgqeykon4kxjxwcg2hxuo2zbahsxzjskjz+he8efdx6i/y5r8x2+5bwx8mpn6bywzelapurod9o8ra+sbmvnw7tro7t39qpzx6nemmkqtoihldjyhhgiswwg4f66aaerkj1okmt4xfewla8eq92gnkqklgiseceltifzpjqb3hnb058crxs9kaeu1b/as/tggmmbjuegn6vpfamcfaciryrnbpdesjnzar6zmqigqmzoe3zvczu4y4trqrzffc/t2re2nmveauuxi7nsteif7n9tibx4c5v2lmmaklrxemse1w8tgecs2ofvnhcnxc3yrpmghcryun5klwl19ejcff86bp3182wq0yjsqcwcmcgw9x0ii7aemafmbwdk/whir6qe/oy9faqexmmfwb+vwbnmyojq==</latexit> <latexit sha1_base64="8d2ytthki0f5v+yac4za3nqypjs=">aaab6nicbvbnswmxej3ur1q/qh69bivgqeykon4kxjxwcg2hxuo2zbahsxzjskjz+he8efdx6i/y5r8x2+5bwx8mpn6bywzelapurod9o8ra+sbmvnw7tro7t39qpzx6nemmkqtoihldjyhhgiswwg4f66aaerkj1okmt4xfewla8eq92gnkqklgiseceltifzpjqb3hnb058crxs9kaeu1b/as/tggmmbjuegn6vpfamcfaciryrnbpdesjnzar6zmqigqmzoe3zvczu4y4trqrzffc/t2re2nmveauuxi7nsteif7n9tibx4c5v2lmmaklrxemse1w8tgecs2ofvnhcnxc3yrpmghcryun5klwl19ejcff86bp3182wq0yjsqcwcmcgw9x0ii7aemafmbwdk/whir6qe/oy9faqexmmfwb+vwbnmyojq==</latexit> <latexit sha1_base64="cdbvlf1xu2cpegiklulsp13g4zo=">aaab6xicbvbns8naej34wetx1aoxxsj4koki6q3gxwnfywttkjvtpf272ytdjvbdf4ixdype/ufe/ddu2xy09cha470zzuafqedauo63s7s8srq2xtoob25t7+xw9vbvdziphj5lrkjaiduouetfccowlsqkcsiwgq6vjn7zezxmibwzoxsdmpyljzijxkq3t92hbqxq1twpyclxclkfao1u5avts1gwozrmuk3bnpuaikfkcczwxo5kglpkhrspbusljveh+ftumtm2so9eibildzmqvydygms9ikpbgvmz0pperpzpa2cmughyltpmogszrvemieni5g/s4wqzesnlkfpc3krygcrkje2nbepw5l9ejp5p7blm3zxv6/uijricwhgcgafnuidraiapdprwdk/w5gjnxxl3pmats04xcwb/4hz+aniqja0=</latexit> <latexit sha1_base64="7gvkpuuddnhfjc+fkbocycmlxkm=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8flx4rgltoq9lsn+3szsbstoqa+ho8efdx6j/y5r9x2+agrq8ghu/nmdmvtkuw6lrftmlldw19o7xz2dre2d2r7h88mcttjpsskyluh9rwkrt3uadk7vrzgoest8lr9drvpxjtrkluczzyikydjslbkfrp7qk36lvrbt2dgswtrya1kndsvb+6/yrlmvfijdwm47kpbjnvkjjkk0o3mzylbeqhvgopoje3qt47dujorninuajtksqz9fdetmnjxnfoo2okq7potcx/ve6g0wwqc5vmybwbl4oystah079jx2joui4touwleythq6opq5toxybglb68tpyz+lxduz2vnrpfgmu4gmm4bq8uoae30aqfgazggv7hzzhoi/pufmxbs04xcwh/4hz+anotja4=</latexit> <latexit sha1_base64="u9emzzho7buvbqhprrx9jegz6wm=">aaab7xicbvbns8naej3ur1q/qh69lbbbg5rebpvw8okxgrgfnptndtou3wzc7kqoot/ciwcvr/4fb/4bt20o2vpg4pheddpzwlqkg6777zrwvtfwn8qbla3tnd296v7bg0kyzbjpepnodkgnl0jxhwvk3k41p3eoessc3uz91hpxritqhscpd2i6ucisjkkvwrqxp56njr1qza27m5bl4hwkbgwavepxt5+wloykmatgddw3xscnggwtfflpzoanli3oghcsvttmjshn507iivx6jeq0lyvkpv6eyglszdgobwdmcwgwvan4n9fjmlokcqhsdlli80vrjgkmzpo76qvngcqxjzrpyw8lbeg1zwgtqtgqvmwxl4l/xr+ue3cxtuajskmmr3amp+dbjttgfprga4mrpmmrvdmp8+k8ox/z1pjtzbzchzifp7wbj1w=</latexit> Recommender systems with Graph ML Embedding Vectors Movies rated by user j All movies User j More layers Rating All users Average Movie k Fully-connected neural network Users who rated movie k

22 22 <latexit sha1_base64="nfvu6g5jsmxhjutq6tx9lkbskk0=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8flx4rgltoq9lsj+3szsbsbsqs+ho8efdx6j/y5r9x2+agrq8ghu/nmdmvtaxxxnw/ndlk6tr6rnmzsrw9s7tx3t940emmgposeylqh1sj4bj9w43adqqqxqhavji6nvqtr1saj/lejfmmyjqqpokmgivdpfw8xrxm1t0zydlxclkdas1e9avbt1gwozrmuk07npuaikfkcczwuulmglpkrnsahusljveh+ezuctmxsp9eibildzmpvydygms9jkpbgvmz1ivevpzp62qmugxyltpmogtzrvemieni9g/s5wqzewnlkfpc3krykcrkje2nykpwfl9ejv5z/aru3z7xgo0ijticwtgcggcx0iabaiipdabwdk/w5gjnxxl3puatjaeyoyq/cd5/ahjbjxi=</latexit> <latexit sha1_base64="jnoyvhbdtoao4sgli/giizbpodu=">aaab6xicbvbns8naej34wetx1aoxxsj4koki6q3gxznunlbqhrlzttqlm03y3ygl9cd48adi1x/kzx/jts1bwx8mpn6bywzemaqujet+o0vlk6tr66wn8ubw9s5uzw//qsezyuizrcsqfvkngkv0dtccw6lcgoccm+hwaui3h1fpnsh7m0oxiglf8ogzaqx099s96vaqbs2dgiwsrybvkndovr46vyrlmurdbnw67bmpcxkqdgccx+vopjglbej72lzu0hh1ke9phznjq/rilchb0pcp+nsip7hwozi0nte1az3vtct/vhzmoosg5zlndeo2wxrlgpietp4mpa6qgtgyhdlf7a2edaiiznh0yjyeb/7lrekf1i5r3u1ztv4v0ijbirzbcxhwdnw4hgb4wkapz/akb45wxpx352pwuuqumwfwb87nd6syjy8=</latexit> <latexit sha1_base64="parslp3scte1/zi1xf9+jnm70s8=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8fl16eisyw2la22027dlmjuxohhp4elx5uvpqpvplv3ly5aoudgcd7m8zmc1mpdlrut1nawv1b3yhvvra2d3b3qvshjybjnom+s2si2ye1xarffrqoetvvnmah5k1wdd31w09cg5gobxynpijpqilimipwus96t71qza27m5bl4hwkbgwavepxt5+wloykmatgddw3xscnggwtfflpzoanli3oghcsvttmjshnp07iivx6jeq0lyvkpv6eyglszdgobwdmcwgwvan4n9fjmlomcqhsdlli80vrjgkmzpo36qvngcqxjzrpyw8lbeg1zwjtqdgqvmwxl4l/vr+qe3fntuajskmmr3amp+dbbttgbprga4mbpmmrvdnsexheny95a8kpzg7hd5zph56gjys=</latexit> <latexit sha1_base64="1uklseon2r96dpcjhfzod3fknam=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leqn4kxjxwnlbqhrlzttqlm03y3qgl9cd48adi1x/kzx/jts1bwx8mpn6bywzemaqujet+o6w19y3nrfj2zwd3b/+genj0qjnmmfrzihlvcalgwsx6hhubnvqhjuob7xb8m/pbt6g0t+sdmaqyxhqoecqznva6z/pev1pz6+4czjv4balbgva/+tubjcyluromqnzdz01nkfnlobm4rfqyjsllyzrerqwsxqidfh7qljxzzucirnmshszv3xm5jbwexkhtjkkz6wvvjv7ndtmtxqu5l2lmullfoigtxcrk9jczcixmiikllclubyvsrbvlxqztssf4yy+vev+ifl337i5rzwarrhlo4btowymgnoewwuadgye8wyu8ocj5cd6dj0vryslmjuepnm8fdeynbw==</latexit> <latexit sha1_base64="8d2ytthki0f5v+yac4za3nqypjs=">aaab6nicbvbnswmxej3ur1q/qh69bivgqeykon4kxjxwcg2hxuo2zbahsxzjskjz+he8efdx6i/y5r8x2+5bwx8mpn6bywzelapurod9o8ra+sbmvnw7tro7t39qpzx6nemmkqtoihldjyhhgiswwg4f66aaerkj1okmt4xfewla8eq92gnkqklgiseceltifzpjqb3hnb058crxs9kaeu1b/as/tggmmbjuegn6vpfamcfaciryrnbpdesjnzar6zmqigqmzoe3zvczu4y4trqrzffc/t2re2nmveauuxi7nsteif7n9tibx4c5v2lmmaklrxemse1w8tgecs2ofvnhcnxc3yrpmghcryun5klwl19ejcff86bp3182wq0yjsqcwcmcgw9x0ii7aemafmbwdk/whir6qe/oy9faqexmmfwb+vwbnmyojq==</latexit> <latexit sha1_base64="8d2ytthki0f5v+yac4za3nqypjs=">aaab6nicbvbnswmxej3ur1q/qh69bivgqeykon4kxjxwcg2hxuo2zbahsxzjskjz+he8efdx6i/y5r8x2+5bwx8mpn6bywzelapurod9o8ra+sbmvnw7tro7t39qpzx6nemmkqtoihldjyhhgiswwg4f66aaerkj1okmt4xfewla8eq92gnkqklgiseceltifzpjqb3hnb058crxs9kaeu1b/as/tggmmbjuegn6vpfamcfaciryrnbpdesjnzar6zmqigqmzoe3zvczu4y4trqrzffc/t2re2nmveauuxi7nsteif7n9tibx4c5v2lmmaklrxemse1w8tgecs2ofvnhcnxc3yrpmghcryun5klwl19ejcff86bp3182wq0yjsqcwcmcgw9x0ii7aemafmbwdk/whir6qe/oy9faqexmmfwb+vwbnmyojq==</latexit> <latexit sha1_base64="cdbvlf1xu2cpegiklulsp13g4zo=">aaab6xicbvbns8naej34wetx1aoxxsj4koki6q3gxwnfywttkjvtpf272ytdjvbdf4ixdype/ufe/ddu2xy09cha470zzuafqedauo63s7s8srq2xtoob25t7+xw9vbvdziphj5lrkjaiduouetfccowlsqkcsiwgq6vjn7zezxmibwzoxsdmpyljzijxkq3t92hbqxq1twpyclxclkfao1u5avts1gwozrmuk3bnpuaikfkcczwxo5kglpkhrspbusljveh+ftumtm2so9eibildzmqvydygms9ikpbgvmz0pperpzpa2cmughyltpmogszrvemieni5g/s4wqzesnlkfpc3krygcrkje2nbepw5l9ejp5p7blm3zxv6/uijricwhgcgafnuidraiapdprwdk/w5gjnxxl3pmats04xcwb/4hz+aniqja0=</latexit> <latexit sha1_base64="7gvkpuuddnhfjc+fkbocycmlxkm=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8flx4rgltoq9lsn+3szsbstoqa+ho8efdx6j/y5r9x2+agrq8ghu/nmdmvtkuw6lrftmlldw19o7xz2dre2d2r7h88mcttjpsskyluh9rwkrt3uadk7vrzgoest8lr9drvpxjtrkluczzyikydjslbkfrp7qk36lvrbt2dgswtrya1kndsvb+6/yrlmvfijdwm47kpbjnvkjjkk0o3mzylbeqhvgopoje3qt47dujorninuajtksqz9fdetmnjxnfoo2okq7potcx/ve6g0wwqc5vmybwbl4oystah079jx2joui4touwleythq6opq5toxybglb68tpyz+lxduz2vnrpfgmu4gmm4bq8uoae30aqfgazggv7hzzhoi/pufmxbs04xcwh/4hz+anotja4=</latexit> <latexit sha1_base64="u9emzzho7buvbqhprrx9jegz6wm=">aaab7xicbvbns8naej3ur1q/qh69lbbbg5rebpvw8okxgrgfnptndtou3wzc7kqoot/ciwcvr/4fb/4bt20o2vpg4pheddpzwlqkg6777zrwvtfwn8qbla3tnd296v7bg0kyzbjpepnodkgnl0jxhwvk3k41p3eoessc3uz91hpxritqhscpd2i6ucisjkkvwrqxp56njr1qza27m5bl4hwkbgwavepxt5+wloykmatgddw3xscnggwtfflpzoanli3oghcsvttmjshn507iivx6jeq0lyvkpv6eyglszdgobwdmcwgwvan4n9fjmlokcqhsdlli80vrjgkmzpo76qvngcqxjzrpyw8lbeg1zwgtqtgqvmwxl4l/xr+ue3cxtuajskmmr3amp+dbjttgfprga4mrpmmrvdmp8+k8ox/z1pjtzbzchzifp7wbj1w=</latexit> Recommender systems with Graph ML Embedding Vectors Back-propagation Movies rated by user j All movies User j More layers Rating All users Average Movie k Fully-connected neural network Users who rated movie k

23 23 Graph convolutional neural networks This system is a graph convolutional neural network. They are general architectures can be used to predict node or edge attributes in arbitrary graphs, not only for recommender systems. Labelled Examples Node features or Node Embeddings X9 X 6 X 8 X 7 X 0 X 10 X 2 X 1 Hidden Layers Z9 Z 6 Z 8 Z 7 Z 0 Z 10 Z 2 Z 1 Y 8 Y 10 Y 2 C X 5 X 4 X 3 F Z 5 Z 4 Z 3 Hamilton et al. Inductive Representation Learning on Large Graphs, NIPS 2017 Kipf and Welling. Semi-supervised classification with graph convolutional networks. ICLR 2017.

24 24 <latexit sha1_base64="gxn56ljn7lqqgtpdsbssxbgfx9i=">aaacjxicbvdnsgmxgmzwv1r/qh69bivgxbirgnoqcl48sqvrc92yznnsg5rnlsm3ytnu03jxvbx4qcj48lvm2z1o60bgmjkvytd+llgg2/6yckvlk6trxfxsxubw9k55d+9br4mireejeamwtzqtxligcbcsfstgql+wpj+4nvjnr6y0j+q9dgpwculp8obtakbyylduoahnnsy9zbcrk9bleyzh2ax2bkkykdnlpi5pcsvc3uv0mmvukveu2fv7crxinjxuui66vx673ygmiznabdg67dgxdfkigfpbspkbabytoia91jzukpdptjpdm8nhruniiflmsmbt9fdeskkth6fvkigbvp73juj/xjub4kktchknwcsdprqkakoej53hllemghgaqqji5q+y9onpduyzjvocm7/yimmcvi+rzt1zpvbl2yiia3sijpgdzlen3aa6aickntergqn368v6sz6sz1m0youz++gpro8fajwm9a==</latexit> <latexit sha1_base64="d1jbitse5hz0pwbyxninozlywog=">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</latexit> Recommender performance How can we measure performance of predicting movie ratings? Mean average error (MAE): Root mean square error (RMSE): Caveats: A lower error between predictions and actual ratings does not mean a method performs well in generating recommendations! To be actually useful recommendations should be serendipitous, diverse, explainable, use context such as time of day and previous queries The user experience is critical! Sean M. McNee et al. Being Accurate is Not Enough: How Accuracy Metrics have hurt Recommender Systems CHI 2006,

25 25 Example: Movie recommendations Traditional Collaborative Filtering (Nearest Neighbour) Collaborative Filtering (Matrix Factorization) MAE RMSE Scalable Prediction Online Training Graph ML MAE RMSE Scalable Prediction Online Training Graph Conv Nets

26 26 Example: Movie recommendations Neural network-based Graph ML methods allow: Leveraging high-performance ML packages such as Tensorflow. Scalable training and prediction to billions of users and items. Integration of ratings Users Movies Rating

27 27 Example: Movie recommendations Neural network-based Graph ML methods allow: Leveraging high-performance ML packages such as tensorflow Scalable training and inference to billions of users and items. Integration of ratings, user and movie information Users Attributes Movies Attributes Rating Attributes Attributes

28 28 Example: Movie recommendations Neural network-based Graph ML methods allow: Leveraging high-performance ML packages such as Tensorflow Scalable training and inference to billions of users and items. Integration of ratings, user and movie information, and social network connections in a single system! Users Attributes Movies Attributes Rating Social Connections Attributes Attributes

29 29 The cold start problem With no previous ratings, collaborative filtering cannot provide sensible recommendations for a new user or movie. User Movies Rating?

30 30 <latexit sha1_base64="nfvu6g5jsmxhjutq6tx9lkbskk0=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8flx4rgltoq9lsj+3szsbsbsqs+ho8efdx6j/y5r9x2+agrq8ghu/nmdmvtaxxxnw/ndlk6tr6rnmzsrw9s7tx3t940emmgposeylqh1sj4bj9w43adqqqxqhavji6nvqtr1saj/lejfmmyjqqpokmgivdpfw8xrxm1t0zydlxclkdas1e9avbt1gwozrmuk07npuaikfkcczwuulmglpkrnsahusljveh+ezuctmxsp9eibildzmpvydygms9jkpbgvmz1ivevpzp62qmugxyltpmogtzrvemieni9g/s5wqzewnlkfpc3krykcrkje2nykpwfl9ejv5z/aru3z7xgo0ijticwtgcggcx0iabaiipdabwdk/w5gjnxxl3puatjaeyoyq/cd5/ahjbjxi=</latexit> <latexit sha1_base64="jnoyvhbdtoao4sgli/giizbpodu=">aaab6xicbvbns8naej34wetx1aoxxsj4koki6q3gxznunlbqhrlzttqlm03y3ygl9cd48adi1x/kzx/jts1bwx8mpn6bywzemaqujet+o0vlk6tr66wn8ubw9s5uzw//qsezyuizrcsqfvkngkv0dtccw6lcgoccm+hwaui3h1fpnsh7m0oxiglf8ogzaqx099s96vaqbs2dgiwsrybvkndovr46vyrlmurdbnw67bmpcxkqdgccx+vopjglbej72lzu0hh1ke9phznjq/rilchb0pcp+nsip7hwozi0nte1az3vtct/vhzmoosg5zlndeo2wxrlgpietp4mpa6qgtgyhdlf7a2edaiiznh0yjyeb/7lrekf1i5r3u1ztv4v0ijbirzbcxhwdnw4hgb4wkapz/akb45wxpx352pwuuqumwfwb87nd6syjy8=</latexit> <latexit sha1_base64="parslp3scte1/zi1xf9+jnm70s8=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8fl16eisyw2la22027dlmjuxohhp4elx5uvpqpvplv3ly5aoudgcd7m8zmc1mpdlrut1nawv1b3yhvvra2d3b3qvshjybjnom+s2si2ye1xarffrqoetvvnmah5k1wdd31w09cg5gobxynpijpqilimipwus96t71qza27m5bl4hwkbgwavepxt5+wloykmatgddw3xscnggwtfflpzoanli3oghcsvttmjshnp07iivx6jeq0lyvkpv6eyglszdgobwdmcwgwvan4n9fjmlomcqhsdlli80vrjgkmzpo36qvngcqxjzrpyw8lbeg1zwjtqdgqvmwxl4l/vr+qe3fntuajskmmr3amp+dbbttgbprga4mbpmmrvdnsexheny95a8kpzg7hd5zph56gjys=</latexit> <latexit sha1_base64="1uklseon2r96dpcjhfzod3fknam=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leqn4kxjxwnlbqhrlzttqlm03y3qgl9cd48adi1x/kzx/jts1bwx8mpn6bywzemaqujet+o6w19y3nrfj2zwd3b/+genj0qjnmmfrzihlvcalgwsx6hhubnvqhjuob7xb8m/pbt6g0t+sdmaqyxhqoecqznva6z/pev1pz6+4czjv4balbgva/+tubjcyluromqnzdz01nkfnlobm4rfqyjsllyzrerqwsxqidfh7qljxzzucirnmshszv3xm5jbwexkhtjkkz6wvvjv7ndtmtxqu5l2lmullfoigtxcrk9jczcixmiikllclubyvsrbvlxqztssf4yy+vev+ifl337i5rzwarrhlo4btowymgnoewwuadgye8wyu8ocj5cd6dj0vryslmjuepnm8fdeynbw==</latexit> <latexit sha1_base64="8d2ytthki0f5v+yac4za3nqypjs=">aaab6nicbvbnswmxej3ur1q/qh69bivgqeykon4kxjxwcg2hxuo2zbahsxzjskjz+he8efdx6i/y5r8x2+5bwx8mpn6bywzelapurod9o8ra+sbmvnw7tro7t39qpzx6nemmkqtoihldjyhhgiswwg4f66aaerkj1okmt4xfewla8eq92gnkqklgiseceltifzpjqb3hnb058crxs9kaeu1b/as/tggmmbjuegn6vpfamcfaciryrnbpdesjnzar6zmqigqmzoe3zvczu4y4trqrzffc/t2re2nmveauuxi7nsteif7n9tibx4c5v2lmmaklrxemse1w8tgecs2ofvnhcnxc3yrpmghcryun5klwl19ejcff86bp3182wq0yjsqcwcmcgw9x0ii7aemafmbwdk/whir6qe/oy9faqexmmfwb+vwbnmyojq==</latexit> <latexit sha1_base64="8d2ytthki0f5v+yac4za3nqypjs=">aaab6nicbvbnswmxej3ur1q/qh69bivgqeykon4kxjxwcg2hxuo2zbahsxzjskjz+he8efdx6i/y5r8x2+5bwx8mpn6bywzelapurod9o8ra+sbmvnw7tro7t39qpzx6nemmkqtoihldjyhhgiswwg4f66aaerkj1okmt4xfewla8eq92gnkqklgiseceltifzpjqb3hnb058crxs9kaeu1b/as/tggmmbjuegn6vpfamcfaciryrnbpdesjnzar6zmqigqmzoe3zvczu4y4trqrzffc/t2re2nmveauuxi7nsteif7n9tibx4c5v2lmmaklrxemse1w8tgecs2ofvnhcnxc3yrpmghcryun5klwl19ejcff86bp3182wq0yjsqcwcmcgw9x0ii7aemafmbwdk/whir6qe/oy9faqexmmfwb+vwbnmyojq==</latexit> <latexit sha1_base64="cdbvlf1xu2cpegiklulsp13g4zo=">aaab6xicbvbns8naej34wetx1aoxxsj4koki6q3gxwnfywttkjvtpf272ytdjvbdf4ixdype/ufe/ddu2xy09cha470zzuafqedauo63s7s8srq2xtoob25t7+xw9vbvdziphj5lrkjaiduouetfccowlsqkcsiwgq6vjn7zezxmibwzoxsdmpyljzijxkq3t92hbqxq1twpyclxclkfao1u5avts1gwozrmuk3bnpuaikfkcczwxo5kglpkhrspbusljveh+ftumtm2so9eibildzmqvydygms9ikpbgvmz0pperpzpa2cmughyltpmogszrvemieni5g/s4wqzesnlkfpc3krygcrkje2nbepw5l9ejp5p7blm3zxv6/uijricwhgcgafnuidraiapdprwdk/w5gjnxxl3pmats04xcwb/4hz+aniqja0=</latexit> <latexit sha1_base64="7gvkpuuddnhfjc+fkbocycmlxkm=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8flx4rgltoq9lsn+3szsbstoqa+ho8efdx6j/y5r9x2+agrq8ghu/nmdmvtkuw6lrftmlldw19o7xz2dre2d2r7h88mcttjpsskyluh9rwkrt3uadk7vrzgoest8lr9drvpxjtrkluczzyikydjslbkfrp7qk36lvrbt2dgswtrya1kndsvb+6/yrlmvfijdwm47kpbjnvkjjkk0o3mzylbeqhvgopoje3qt47dujorninuajtksqz9fdetmnjxnfoo2okq7potcx/ve6g0wwqc5vmybwbl4oystah079jx2joui4touwleythq6opq5toxybglb68tpyz+lxduz2vnrpfgmu4gmm4bq8uoae30aqfgazggv7hzzhoi/pufmxbs04xcwh/4hz+anotja4=</latexit> <latexit sha1_base64="u9emzzho7buvbqhprrx9jegz6wm=">aaab7xicbvbns8naej3ur1q/qh69lbbbg5rebpvw8okxgrgfnptndtou3wzc7kqoot/ciwcvr/4fb/4bt20o2vpg4pheddpzwlqkg6777zrwvtfwn8qbla3tnd296v7bg0kyzbjpepnodkgnl0jxhwvk3k41p3eoessc3uz91hpxritqhscpd2i6ucisjkkvwrqxp56njr1qza27m5bl4hwkbgwavepxt5+wloykmatgddw3xscnggwtfflpzoanli3oghcsvttmjshn507iivx6jeq0lyvkpv6eyglszdgobwdmcwgwvan4n9fjmlokcqhsdlli80vrjgkmzpo76qvngcqxjzrpyw8lbeg1zwgtqtgqvmwxl4l/xr+ue3cxtuajskmmr3amp+dbjttgfprga4mrpmmrvdmp8+k8ox/z1pjtzbzchzifp7wbj1w=</latexit> Recommender systems with Graph ML Embedding Vectors Back-propagation Movies rated by user j All movies User j More layers Rating All users Average Movie k Fully-connected neural network Users who rated movie k

31 31 <latexit sha1_base64="rcegekgpro8h/yrziprx88ds5gw=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8fl56korgfnptndtmu3wzc7kqoot/biwcvr/4jb/4bt20o2vpg4pheddpzwlqkg6777zrwvtfwn8qbla3tnd296v7bo0kyzbjpepnodkgnl0jxhwvk3k41p3eoesscxu/91hpxritqaccpd2i6ucisjkkv7qpeba9ac+vudgszeawpqyfmr/rv7scsi7lcjqkxhc9nmciprsekn1s6meepzsm64b1lfy25cflzqrnyypu+irjtsygzqb8nchobm45d2xlthjpfbyr+53uyjc6dxkg0q67yffgusyijmf5n+kjzhnjscwva2fsjg1jngdp0kjyeb/hlzekf1a/q3t15rdeo0ijderzdkxhwaq24gsb4wgaaz/akb450xpx352pewnkkmup4a+fzb4lyjx0=</latexit> <latexit sha1_base64="parslp3scte1/zi1xf9+jnm70s8=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8fl16eisyw2la22027dlmjuxohhp4elx5uvpqpvplv3ly5aoudgcd7m8zmc1mpdlrut1nawv1b3yhvvra2d3b3qvshjybjnom+s2si2ye1xarffrqoetvvnmah5k1wdd31w09cg5gobxynpijpqilimipwus96t71qza27m5bl4hwkbgwavepxt5+wloykmatgddw3xscnggwtfflpzoanli3oghcsvttmjshnp07iivx6jeq0lyvkpv6eyglszdgobwdmcwgwvan4n9fjmlomcqhsdlli80vrjgkmzpo36qvngcqxjzrpyw8lbeg1zwjtqdgqvmwxl4l/vr+qe3fntuajskmmr3amp+dbbttgbprga4mbpmmrvdnsexheny95a8kpzg7hd5zph56gjys=</latexit> <latexit sha1_base64="9rlutxdm2qhb72hiu61llonsqz0=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leqn4kxjxwnlbqhrlzbtqlm03ynqgl9cd48adi1x/kzx/jts1bwx8mpn6bywzemeph0hw/ndla+sbmvnm7sro7t39qptx6nemmgfdzihpdcanhuijuo0djo6nmna4lb4fjm5nffulaieq94ctlquyhskscubtsfdt3+twaw3fnikvek0gncrt61a/eigfzzbuysy3pem6kqu41cib5tnllde8pg9mh71qqamxnkm9pnzizqwxilghbcslc/t2r09iysrzazpjiycx7m/e/r5thdbxkqquzcsuwi6jmekzi7g8yejozlbnlknpc3kryigrk0kztssf4yy+vev+ifl337i5rzwarrhlo4btowymgnoewwuadgye8wyu8odj5cd6dj0vryslmjuepnm8fxygnya==</latexit> <latexit sha1_base64="9dh+jvp2g9br00tuyokv9c79kvo=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8flx4rgltoq9lsj+nszsbsboqs+ho8efdx6j/y5r9x2+ag1qcdj/dmmjkxzojr47pftmvldw19o7pz29re2d2r7x886drxdh2wilr1q6prcim+4uzgn1nik1bgjxxfz/zoiyrnu3lvjhkgcy0ljzijxkp38cab1btu052d/cvesrpqoj2of/ahkcstliyjqnxpczmtffqzzgroa/1cy0bzmmbys1tsbhvqze+dkhordemuklvskln6c6kgidatjlsdctujveznxp+8xm6iy6dgmssnsrzyfowcmjtm/izdrpazmbgemsxtrysnqklm2hrqngrv+ew/xd9rxjw92/ngq1wmuyujoizt8oacwnadbfcbqqxp8akvjncentfnfdfaccqzq/gf5+mbxwanyq==</latexit> <latexit sha1_base64="8d2ytthki0f5v+yac4za3nqypjs=">aaab6nicbvbnswmxej3ur1q/qh69bivgqeykon4kxjxwcg2hxuo2zbahsxzjskjz+he8efdx6i/y5r8x2+5bwx8mpn6bywzelapurod9o8ra+sbmvnw7tro7t39qpzx6nemmkqtoihldjyhhgiswwg4f66aaerkj1okmt4xfewla8eq92gnkqklgiseceltifzpjqb3hnb058crxs9kaeu1b/as/tggmmbjuegn6vpfamcfaciryrnbpdesjnzar6zmqigqmzoe3zvczu4y4trqrzffc/t2re2nmveauuxi7nsteif7n9tibx4c5v2lmmaklrxemse1w8tgecs2ofvnhcnxc3yrpmghcryun5klwl19ejcff86bp3182wq0yjsqcwcmcgw9x0ii7aemafmbwdk/whir6qe/oy9faqexmmfwb+vwbnmyojq==</latexit> <latexit sha1_base64="8d2ytthki0f5v+yac4za3nqypjs=">aaab6nicbvbnswmxej3ur1q/qh69bivgqeykon4kxjxwcg2hxuo2zbahsxzjskjz+he8efdx6i/y5r8x2+5bwx8mpn6bywzelapurod9o8ra+sbmvnw7tro7t39qpzx6nemmkqtoihldjyhhgiswwg4f66aaerkj1okmt4xfewla8eq92gnkqklgiseceltifzpjqb3hnb058crxs9kaeu1b/as/tggmmbjuegn6vpfamcfaciryrnbpdesjnzar6zmqigqmzoe3zvczu4y4trqrzffc/t2re2nmveauuxi7nsteif7n9tibx4c5v2lmmaklrxemse1w8tgecs2ofvnhcnxc3yrpmghcryun5klwl19ejcff86bp3182wq0yjsqcwcmcgw9x0ii7aemafmbwdk/whir6qe/oy9faqexmmfwb+vwbnmyojq==</latexit> <latexit sha1_base64="cdbvlf1xu2cpegiklulsp13g4zo=">aaab6xicbvbns8naej34wetx1aoxxsj4koki6q3gxwnfywttkjvtpf272ytdjvbdf4ixdype/ufe/ddu2xy09cha470zzuafqedauo63s7s8srq2xtoob25t7+xw9vbvdziphj5lrkjaiduouetfccowlsqkcsiwgq6vjn7zezxmibwzoxsdmpyljzijxkq3t92hbqxq1twpyclxclkfao1u5avts1gwozrmuk3bnpuaikfkcczwxo5kglpkhrspbusljveh+ftumtm2so9eibildzmqvydygms9ikpbgvmz0pperpzpa2cmughyltpmogszrvemieni5g/s4wqzesnlkfpc3krygcrkje2nbepw5l9ejp5p7blm3zxv6/uijricwhgcgafnuidraiapdprwdk/w5gjnxxl3pmats04xcwb/4hz+aniqja0=</latexit> <latexit sha1_base64="7gvkpuuddnhfjc+fkbocycmlxkm=">aaab6xicbvbns8naej3ur1q/qh69lbbbu0leug8flx4rgltoq9lsn+3szsbstoqa+ho8efdx6j/y5r9x2+agrq8ghu/nmdmvtkuw6lrftmlldw19o7xz2dre2d2r7h88mcttjpsskyluh9rwkrt3uadk7vrzgoest8lr9drvpxjtrkluczzyikydjslbkfrp7qk36lvrbt2dgswtrya1kndsvb+6/yrlmvfijdwm47kpbjnvkjjkk0o3mzylbeqhvgopoje3qt47dujorninuajtksqz9fdetmnjxnfoo2okq7potcx/ve6g0wwqc5vmybwbl4oystah079jx2joui4touwleythq6opq5toxybglb68tpyz+lxduz2vnrpfgmu4gmm4bq8uoae30aqfgazggv7hzzhoi/pufmxbs04xcwh/4hz+anotja4=</latexit> <latexit sha1_base64="u9emzzho7buvbqhprrx9jegz6wm=">aaab7xicbvbns8naej3ur1q/qh69lbbbg5rebpvw8okxgrgfnptndtou3wzc7kqoot/ciwcvr/4fb/4bt20o2vpg4pheddpzwlqkg6777zrwvtfwn8qbla3tnd296v7bg0kyzbjpepnodkgnl0jxhwvk3k41p3eoessc3uz91hpxritqhscpd2i6ucisjkkvwrqxp56njr1qza27m5bl4hwkbgwavepxt5+wloykmatgddw3xscnggwtfflpzoanli3oghcsvttmjshn507iivx6jeq0lyvkpv6eyglszdgobwdmcwgwvan4n9fjmlokcqhsdlli80vrjgkmzpo76qvngcqxjzrpyw8lbeg1zwgtqtgqvmwxl4l/xr+ue3cxtuajskmmr3amp+dbjttgfprga4mrpmmrvdmp8+k8ox/z1pjtzbzchzifp7wbj1w=</latexit> Recommender systems with Graph ML Back-propagation Input Features Movies rated by user j Movie Features User j More layers Rating User Features Average Movie k Fully-connected neural network Users who rated movie k

32 32 The cold start problem Extreme example: two separated groups of users and movies. User Attributes Movies Attributes Rating Train Attributes Attributes Attributes Attributes Test Attributes Attributes

33 33 Movie recommendations: Cold Start Traditional MAE RMSE Content Based Collaborative Filtering (Nearest Neighbour) Collaborative Filtering (Matrix Factorization) Graph ML MAE RMSE Graph Conv Net

34 34 Recommender systems in practice

35 35 Conclusions General-purpose algorithms for machine learning on graphs are becoming competitive with niche and purpose built methods Built on deep learning technology Support online and batch training Scalable to billions of nodes Support multiple node and edge types Can transfer knowledge from one graph to another Can use attributes of entities Many problems living in tables are moving to graphs Drug interactions Recommender systems Social networking Spam detection Entity resolution Fraud detection Biomedical interactions

36 Thank you!

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