Visual　affordance　studies　what　kind　of　interaction　is　possible　and　whether　the　interaction　is　reasonable　in　the　current　environment　from　an　image/video.　When　inferring　affordances　of　objects,　semantics　and　relations　of　objects　in　the　environment　should　be　considered,　and　graph　is　usually　used　for　modeling　the　environment　context　for　object.　Considering　the　weight　of　edge　in　graph　describes　the　amount　of　contributed　information　between　objects　during　affordance　reasoning,　this　paper　proposes　VAR-Net　(Visual　Affordance　Reasoning　Network)　which　models　the　weights　as　graph　attention　coefficients　and　learns　the　weights　based　on　objects＇　semantic　and　visual　features　implying　their　affordances.　VAR-Net　achieves　higher　accuracy　on　COCO-Tasks　and　ADE-Affordance　datasets.　Experiments　also　explain　the　meaning　of　edge　weights　in　VAR-Net.　For　a　definite　affordance,　an　object　commits　it　more,　the　edges　linking　from　it　to　other　objects　have　larger　weights　and　vice　versa,　which　makes　objects＇　features　distinguishable　for　inferring　affordances.　©　2022　IEEE.